"""TAU Urban Acoustic Scenes 2022 Mobile Loader
.. admonition:: Dataset Info
:class: dropdown
**TAU Urban Acoustic Scenes 2022 Mobile, Development and Evaluation datasets**
`Audio Research Group,
Tampere University of Technology <http://arg.cs.tut.fi/>`__
*Authors*
* `Toni Heittola <http://www.cs.tut.fi/~heittolt/>`__
* `Annamaria Mesaros <http://www.cs.tut.fi/~mesaros/>`__
* `Tuomas Virtanen <http://www.cs.tut.fi/~tuomasv/>`__
*Recording and annotation*
* Henri Laakso
* Ronal Bejarano Rodriguez
* Toni Heittola
*Links*
* `Development dataset <https://zenodo.org/record/6337421>`__
* `Evaluation dataset <https://zenodo.org/record/7870258>`__
*Dataset*
TAU Urban Acoustic Scenes 2022 Mobile development dataset consists of
1-seconds audio segments from 10 acoustic scenes:
* Airport - `airport`
* Indoor shopping mall - `shopping_mall`
* Metro station - `metro_station`
* Pedestrian street - `street_pedestrian`
* Public square - `public_square`
* Street with medium level of traffic - `street_traffic`
* Travelling by a tram - `tram`
* Travelling by a bus - `bus`
* Travelling by an underground metro - `metro`
* Urban park - `park`
The dataset contains the same material than TAU Urban Acoustic Scenes 2020 Mobile development dataset,
10-second audio segments have been split into non-overlapping 1-second segments for 2022 version of the dataset.
A detailed description of the data recording and annotation procedure is
available in:
.. code-block:: latex
Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen.
"Acoustic scene classification in DCASE 2020 Challenge:
generalization across devices and low complexity solutions",
In Proceedings of the Detection and Classification of Acoustic
Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan, 2020.
Recordings were made with three devices (A, B and C) that captured audio
simultaneously and 6 simulated devices (S1-S6). Each acoustic scene has
1440 segments (240 minutes of audio) recorded with device A (main device)
and 108 segments of parallel audio (18 minutes) each recorded with devices
B,C, and S1-S6.
*Development dataset*
The dataset contains in total 64 hours of audio.
*Evaluation dataset*
The dataset contains in total 33 hours of audio.
The dataset was collected by Tampere University of Technology between
05/2018 -11/2018. The data collection has received funding from
the European Research Council under the `ERC <https://erc.europa.eu/1>`_
Grant Agreement 637422 EVERYSOUND.
*Preparation of the dataset*
The dataset was recorded in 12 large European cities: Amsterdam, Barcelona,
Helsinki, Lisbon, London, Lyon, Madrid, Milan, Prague, Paris, Stockholm,
and Vienna. For all acoustic scenes, audio was captured in multiple
locations: different streets, different parks, different shopping malls.
In each location, multiple 2-3 minute long audio recordings were captured
in a few slightly different positions (2-4) within the selected location.
Collected audio material was cut into segments of 10 seconds length.
The main recording device (referred to as device A) consists of a binaural
`Soundman OKM IIKlassik/studio A3 <http://www.soundman.de/en/products/>`_
electret in-ear microphone and a `Zoom F8
<https://www.zoom.co.jp/products/handy-recorder/zoom-f8-multitrack-field-recorder>`_
audio recorder using 48 kHz sampling rate and 24 bit resolution. During the
recording, the microphones were worn by the recording person in the ears,
and head movement was kept to minimum.
Devices B and C are commonly available customer devices (e.g. smartphones,
cameras) and were handled in typical ways (e.g. hand held). The audio
recordings from these devices are of different quality than device A. All
simultaneous recordings are time synchronized.
Post-processing of the recorded audio involves aspects related to privacy
of recorded individuals, and possible errors in the recording process.
The material was screened for content, and segments containing close
microphone conversation were eliminated. Some interferences from mobile
phones are audible, but are considered part of real-world recording
process. In addition, data from device A was resampled and averaged into a
single channel, to align with the properties of the data recorded with
devices B and C.
Additionally, 11 mobile devices S1-S11 are simulated using the audio
recorded with device A, impulse responses recorded with real devices, and
additional dynamic range compression, in order to simulate realistic
recordings. A recording from device A is processed through convolution
with the selected Si impulse response, then processed with a selected set
of parameters for dynamic range compression (device specific). The impulse
responses are proprietary data and will not be published.
All provided audio data is single-channel, having a 44.1 KHz sampling rate,
and 24 bit resolution.
A subset of the dataset has been previously published as TUT Urban Acoustic
Scenes 2019 Development dataset. Audio segment filenames are retained for
the segments coming from this dataset.
**Dataset statistics**
The development set contains data from 10 cities and 9 devices: 3 real
devices (A, B, C) and 6 simulated devices (S1-S6). Data from devices B, C
and S1-S6 consists of randomly selected segments from the simultaneous
recordings, therefore all overlap with the data from device A, but not
necessarily with each other. The total amount of audio in the development
set is **64 hours**. The evaluation dataset (TAU Urban Acoustic Scenes 2022
Mobile evaluation) contains data from all 12 cities, and five new devices
(not available in the development set): real device D and simulated devices
S7-S11.
**Device A**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 14400 1280 1490 1440 1450 1440 1440 1560 1440 1580 1280
Bus 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Metro 14400 1410 1440 1440 1460 1440 1440 1440 1440 1450 1440
Metro station 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Park 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Public square 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Shopping mall 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Street, pedestrian 14400 1450 1450 1440 1450 1440 1440 1440 1440 1450 1400
Street, traffic 14400 1440 1440 1440 1440 1440 1440 1440 1440 1440 1440
Tram 14400 1430 1450 1440 1440 1440 1440 1440 1440 1440 1440
**Total** **144000** **14210** **14470** **14400** **14440** **14400** **14400** **14520** **14400** **14560** **14200**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 40 4 3 4 3 4 4 4 6 5 3
Bus 71 4 4 11 7 7 7 11 10 6 4
Metro 67 3 5 11 4 9 8 9 10 4 4
Metro station 57 5 6 4 12 5 4 9 4 4 4
Park 41 4 4 4 4 4 4 4 4 5 4
Public_square 43 4 4 4 4 5 4 4 6 4 4
Shopping mall 36 4 4 4 2 3 3 4 4 4 4
Street, pedestrian 46 7 4 4 4 4 5 5 5 4 4
Street, traffic 43 4 4 4 5 4 6 4 4 4 4
Tram 70 4 4 6 9 7 11 9 11 5 4
**Total** **514** **43** **42** **56** **54** **52** **56** **63** **65** **45** **39**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device B**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1070 110 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1070 110 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10780** **1180** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 36 3 3 4 3 3 4 4 5 4 3
Bus 57 4 4 9 7 6 5 8 7 3 4
Metro 47 3 4 6 4 6 5 6 6 4 4
Metro station 45 4 4 3 8 5 3 7 3 4 4
Park 37 4 4 4 4 4 3 4 3 3 4
Public_square 37 3 4 4 4 5 3 4 4 3 3
Shopping mall 34 4 4 4 2 3 3 4 4 3 3
Street, pedestrian 43 6 3 4 4 4 5 5 4 4 4
Street, traffic 41 4 4 4 4 4 6 4 4 4 4
Tram 50 4 4 5 6 5 5 7 7 3 4
**Total** **427** **39** **37** **47** **46** **44** **42** **53** **47** **35** **37**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device C**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1070 110 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1070 120 120 120 100 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1070 110 120 120 110 110 100 100 100 100 100
**Total** **10770** **1180** **1200** **1200** **1090** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 38 4 3 4 3 3 4 4 5 5 3
Bus 50 4 4 7 6 5 4 7 7 3 3
Metro 54 3 3 6 4 9 6 7 8 4 4
Metro station 48 5 3 4 8 5 4 7 4 4 4
Park 39 4 4 4 4 4 4 4 4 3 4
Public_square 40 4 3 4 4 4 4 4 6 3 4
Shopping mall 35 4 4 4 2 3 3 4 4 3 4
Street, pedestrian 41 6 3 4 4 3 5 4 5 4 3
Street, traffic 40 4 3 4 4 4 6 4 4 4 3
Tram 51 4 4 5 6 4 8 6 7 3 4
**Total** **436** **42** **34** **46** **45** **44** **48** **51** **54** **36** **36**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S1**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 37 4 3 4 3 4 4 4 4 4 3
Bus 54 4 4 8 6 6 6 7 6 3 4
Metro 50 3 3 8 4 7 6 6 6 4 3
Metro station 48 5 4 4 9 5 4 5 4 4 4
Park 36 4 4 4 4 3 4 3 3 3 4
Public_square 37 4 4 4 4 4 4 3 3 3 4
Shopping mall 33 4 4 4 2 3 3 3 3 3 4
Street, pedestrian 40 6 3 4 4 3 5 2 5 4 4
Street, traffic 40 4 4 4 4 4 6 3 3 4 4
Tram 52 4 4 5 7 6 7 6 6 3 4
**Total** **427** **42** **37** **49** **47** **45** **49** **42** **43** **35** **38**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S2**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 36 3 3 4 3 4 4 4 4 4 3
Bus 58 4 4 9 6 6 7 9 6 3 4
Metro 55 3 3 10 4 8 8 5 7 4 3
Metro station 49 5 4 4 7 5 4 8 4 4 4
Park 38 4 4 4 4 4 4 4 4 2 4
Public_square 41 4 4 4 4 5 4 4 5 3 4
Shopping mall 34 4 4 3 2 3 3 4 4 3 4
Street, pedestrian 42 7 3 4 4 3 5 5 4 4 3
Street, traffic 42 4 4 4 5 4 6 4 4 4 3
Tram 51 4 4 5 7 6 7 7 4 3 4
**Total** **446** **42** **37** **51** **46** **48** **52** **54** **46** **34** **36**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S3**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 36 3 3 4 3 4 4 4 4 4 3
Bus 50 4 4 6 5 6 6 7 5 3 4
Metro 50 3 3 10 4 5 6 4 8 3 4
Metro station 44 4 4 4 6 5 4 7 3 4 3
Park 39 4 4 4 4 4 4 4 4 3 4
Public_square 39 4 4 3 4 5 4 4 4 3 4
Shopping mall 32 4 4 3 2 3 3 4 3 3 3
Street, pedestrian 39 6 3 3 4 4 4 5 3 4 3
Street, traffic 40 4 4 4 5 4 5 4 3 3 4
Tram 50 4 4 5 8 5 7 6 5 3 3
**Total** **419** **40** **37** **46** **45** **45** **47** **49** **42** **33** **35**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S4**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 36 3 3 4 3 4 4 4 4 4 3
Bus 53 4 4 9 5 6 5 6 7 3 4
Metro 50 3 2 8 4 7 6 7 6 4 3
Metro station 47 5 4 4 7 5 4 6 4 4 4
Park 38 4 3 4 4 4 4 4 4 3 4
Public_square 38 4 4 3 3 5 4 4 4 3 4
Shopping mall 35 4 4 4 2 3 3 4 4 3 4
Street, pedestrian 42 7 3 3 4 4 4 4 5 4 4
Street, traffic 41 4 4 4 4 4 5 4 4 4 4
Tram 51 4 4 6 6 7 5 7 5 3 4
**Total** **431** **42** **35** **49** **42** **49** **44** **50** **47** **35** **38**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S5**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 38 4 3 4 3 4 4 3 5 5 3
Bus 54 3 4 6 6 6 7 8 7 3 4
Metro 51 3 3 7 4 8 6 6 7 4 3
Metro station 45 5 3 3 7 4 4 7 4 4 4
Park 36 3 4 3 3 4 4 4 4 3 4
Public_square 39 3 4 3 4 4 4 4 6 3 4
Shopping mall 33 3 4 3 2 3 3 4 4 3 4
Street, pedestrian 42 6 3 4 4 4 4 5 5 4 3
Street, traffic 38 3 3 4 4 4 4 4 4 4 4
Tram 50 4 4 4 6 5 8 7 6 3 3
**Total** **426** **37** **35** **41** **43** **46** **48** **52** **52** **36** **36**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Device S6**
*Audio segments*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Segments Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 1080 120 120 120 110 110 100 100 100 100 100
Bus 1080 120 120 120 110 110 100 100 100 100 100
Metro 1080 120 120 120 110 110 100 100 100 100 100
Metro station 1080 120 120 120 110 110 100 100 100 100 100
Park 1080 120 120 120 110 110 100 100 100 100 100
Public square 1080 120 120 120 110 110 100 100 100 100 100
Shopping mall 1080 120 120 120 110 110 100 100 100 100 100
Street, pedestrian 1080 120 120 120 110 110 100 100 100 100 100
Street, traffic 1080 120 120 120 110 110 100 100 100 100 100
Tram 1080 120 120 120 110 110 100 100 100 100 100
**Total** **10800** **1200** **1200** **1200** **1100** **1100** **1000** **1000** **1000** **1000** **1000**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
*Recording locations*
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Scene class Locations Barcelona Helsinki Lisbon London Lyon Milan Paris Prague Stockholm Vienna
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
Airport 36 4 3 4 3 4 3 3 5 4 3
Bus 55 3 4 9 7 6 5 9 6 2 4
Metro 51 3 2 7 4 7 6 7 8 3 4
Metro station 47 5 4 4 9 3 3 7 4 4 4
Park 37 3 4 4 4 4 3 4 4 3 4
Public_square 39 4 4 4 4 4 3 4 5 3 4
Shopping mall 33 3 4 4 2 3 2 4 4 3 4
Street, pedestrian 39 5 3 4 4 3 4 4 4 4 4
Street, traffic 39 3 4 3 4 4 5 4 4 4 4
Tram 56 4 4 6 7 6 7 6 9 3 4
**Total** **432** **37** **35** **49** **48** **44** **41** **52** **53** **33** **39**
=================== ========== ========== ========== ========= ========= ========== ========= ========= ========= ========== ==========
**Usage**
The partitioning of the data was done based on the location of the original
recordings. All segments recorded at the same location were included into a
single subset - either **development dataset** or **evaluation dataset**.
For each acoustic scene, 1440 segments recorded with device A, 108 segments
recorded with device B, C and S1-S6 were included in the development
dataset provided here. Evaluation dataset is provided separately.
*Training / test setup*
A suggested training/test partitioning of the development set is provided
in order to make results reported with this dataset uniform. The
partitioning is done such that the segments recorded at the same location
are included into the same subset - either training or testing. The
partitioning is done aiming for a 70/30 ratio between the number of
segments in training and test subsets while taking into account recording
locations, and selecting the closest available option.
Data from devices A, B, C, S1, S2, S3 are available in both training and
test sets. Audio segments coming from devices S4, S5, and S6 are used only
for testing. Since the dataset includes balanced amount of material from
devices (B, C, and S1-S6), this partitioning will leave a small subset of
data from devices S4-S6 unused in the training / test setup. This material
can be used when using full dataset to train the system and testing it with
evaluation dataset.
The setup is provided with the dataset in the directory `evaluation_setup`.
*Statistics*
=================== ================= ================== ================ ================= ================== ===================
Scene class Train / Segments Train / Locations Test / Segments Test / Locations Unused / Segments Unused / Locations
=================== ================= ================== ================ ================= ================== ===================
Airport 13930 28 2960 12 6130 40
Bus 14000 51 2970 19 6070 66
Metro 13820 47 2970 20 6250 65
Metro station 13800 40 2970 16 6270 55
Park 14290 30 2970 11 5780 39
Public square 14270 31 2970 12 5790 42
Shopping mall 13730 26 2970 10 6330 35
Street, pedestrian 13860 32 2970 14 6210 45
Street, traffic 14130 31 2970 12 5940 43
Tram 13790 49 2960 20 6280 67
**Total** **139620** **365** **29680** **146** **610500** **497**
=================== ================= ================== ================ ================= ================== ===================
*Number of segments in train / test setup*
=================== ================= ========================== ================ ================================= =====================
Scene class Train / Device A Train / Device B,C,S1-S3 Test / Device A Test / Device Device B,C,S1-S3 Test / Device S4-S6
=================== ================= ========================== ================ ================================= =====================
Airport 10190 750 330 330 330
Bus 10250 750 330 330 330
Metro 10070 750 330 330 330
Metro station 10050 750 330 330 330
Park 10540 750 330 330 330
Public square 10530 750 330 330 330
Shopping mall 9990 750 330 330 330
Street, pedestrian 10110 750 330 330 330
Street, traffic 10380 750 330 330 330
Tram 10040 750 330 330 330
**Total** 102150 **7500** **3300** **16500** **9900**
=================== ================= ========================== ================ ================================= =====================
**License**
License permits free academic usage. Any commercial use is strictly prohibited. For commercial use, contact dataset authors.
Copyright (c) 2022 Tampere University and its licensors
All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty
fees, to use and copy the TAU Urban Acoustic Scenes 2022 Mobile (“Work”) described in this document
and composed of audio and metadata. This grant is only for experimental and non-commercial
purposes, provided that the copyright notice in its entirety appear in all copies of this Work,
and the original source of this Work, (Audio Research Group at Tampere University of Technology),
is acknowledged in any publication that reports research using this Work.
Any commercial use of the Work or any part thereof is strictly prohibited.
Commercial use include, but is not limited to:
- selling or reproducing the Work
- selling or distributing the results or content achieved by use of the Work
- providing services by using the Work.
IN NO EVENT SHALL TAMPERE UNIVERSITY OR ITS LICENSORS BE LIABLE TO ANY PARTY
FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE
OF THIS WORK AND ITS DOCUMENTATION, EVEN IF TAMPERE UNIVERSITY OR ITS
LICENSORS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
TAMPERE UNIVERSITY AND ALL ITS LICENSORS SPECIFICALLY DISCLAIMS ANY
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE. THE WORK PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND
THE TAMPERE UNIVERSITY HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,
UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
"""
import os
from typing import BinaryIO, Optional, TextIO, Tuple
import librosa
import numpy as np
import csv
from soundata import download_utils, jams_utils, core, annotations, io
BIBTEX = """
@inproceedings{Heittola:DCASE:20,
Address = {Tokyo, Japan},
Author = {Mesaros, A. and Heittola, T. and Virtanen, T.},
Booktitle = {Proceedings of the Detection and Classification of Acoustic
Scenes and Events 2020 Workshop (DCASE2020},
Month = {November},
Pages = {56--60},
Title = {Acoustic scene classification in DCASE 2020 Challenge:
generalization across devices and low complexity solutions},
Year = {2020}}
"""
REMOTES = {
"development.audio.1": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.1.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.1.zip?download=1",
checksum="8be14bdbd844481dce059ab31fbd2239",
),
"development.audio.2": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.2.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.2.zip?download=1",
checksum="e821abda49b069248bb75eab39df7575",
),
"development.audio.3": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.3.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.3.zip?download=1",
checksum="9c7897f78bb42ca0008936daddf3f8f3",
),
"development.audio.4": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.4.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.4.zip?download=1",
checksum="53ec3e37c0fc0be75f53c5abbc5300b8",
),
"development.audio.5": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.5.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.5.zip?download=1",
checksum="528422c33c5d10263203d2c61cb1f3be",
),
"development.audio.6": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.6.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.6.zip?download=1",
checksum="4759a9465c903e13e57d5e82e791fc28",
),
"development.audio.7": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.7.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.7.zip?download=1",
checksum="6d545fc83a55a17856a9ccc1ac9aff6b",
),
"development.audio.8": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.8.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.8.zip?download=1",
checksum="3a2916a9b5d3f43cc7a22a62b180b0d9",
),
"development.audio.9": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.9.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.9.zip?download=1",
checksum="071f664c86639ad730f7be4e3c39d886",
),
"development.audio.10": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.10.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.10.zip?download=1",
checksum="e0839d019e9b77ecbcbb0eff85965530",
),
"development.audio.11": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.11.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.11.zip?download=1",
checksum="fc60f1c83726be3963ae14fbcb95eeb4",
),
"development.audio.12": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.12.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.12.zip?download=1",
checksum="02246547f86c0a814dc63ec65d7033bb",
),
"development.audio.13": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.13.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.13.zip?download=1",
checksum="88ae9ef884665140d1572d8061725e00",
),
"development.audio.14": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.14.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.14.zip?download=1",
checksum="c3f400d74ae90e5190dc518cde43de47",
),
"development.audio.15": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.15.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.15.zip?download=1",
checksum="85594c6fe4f91c10aa2a410f748103b8",
),
"development.audio.16": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.audio.16.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.audio.16.zip?download=1",
checksum="6e2df8438c69f6789414aeba8cbad9d8",
),
"development.doc": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.doc.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.doc.zip?download=1",
checksum="9c4f610bdb5d3e3e72a17d393a26452a",
),
"development.meta": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2022-mobile-development.meta.zip",
url="https://zenodo.org/record/6337421/files/TAU-urban-acoustic-scenes-2022-mobile-development.meta.zip?download=1",
checksum="419b6ff6570f1030730352dc80cd8d15",
),
"evaluation.audio.1": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.1.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.1.zip?download=1",
checksum="9f085fb1f4678a2f54eb3ef89f68693c",
),
"evaluation.audio.2": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.2.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.2.zip?download=1",
checksum="f463be49f71247c4eb683a0fb36e3ae5",
),
"evaluation.audio.3": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.3.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.3.zip?download=1",
checksum="e711abc5c287d5f1c768848c50b38c49",
),
"evaluation.audio.4": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.4.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.4.zip?download=1",
checksum="fc4b798897a067b67e80799bef4a3b9f",
),
"evaluation.audio.5": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.5.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.5.zip?download=1",
checksum="f317da857edaa9de7a1b8ef5d67f15e7",
),
"evaluation.audio.6": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.6.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.6.zip?download=1",
checksum="9b533e27c01b603773e61bd4ad2d2d97",
),
"evaluation.audio.7": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.7.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.7.zip?download=1",
checksum="bbc3c7a86d7c04149059525808fd9389",
),
"evaluation.audio.8": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.8.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.audio.8.zip?download=1",
checksum="658213f02ee4737fb17d11daaa1bc0b4",
),
"evaluation.doc": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.doc.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.doc.zip?download=1",
checksum="6cbdacf7eed2718a987f88023c1bc104",
),
"evaluation.meta": download_utils.RemoteFileMetadata(
filename="TAU-urban-acoustic-scenes-2023-mobile-evaluation.meta.zip",
url="https://zenodo.org/record/7870258/files/TAU-urban-acoustic-scenes-2023-mobile-evaluation.meta.zip?download=1",
checksum="21deb3451741b6dab46287eb220261c2",
),
}
LICENSE_INFO = """
License permits free academic usage. Any commercial use is strictly prohibited. For commercial use, contact dataset authors.
Copyright (c) 2022 Tampere University and its licensors
All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty
fees, to use and copy the TAU Urban Acoustic Scenes 2022 Mobile (“Work”) described in this document
and composed of audio and metadata. This grant is only for experimental and non-commercial
purposes, provided that the copyright notice in its entirety appear in all copies of this Work,
and the original source of this Work, (Audio Research Group at Tampere University of Technology),
is acknowledged in any publication that reports research using this Work.
Any commercial use of the Work or any part thereof is strictly prohibited.
Commercial use include, but is not limited to:
- selling or reproducing the Work
- selling or distributing the results or content achieved by use of the Work
- providing services by using the Work.
IN NO EVENT SHALL TAMPERE UNIVERSITY OR ITS LICENSORS BE LIABLE TO ANY PARTY
FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE
OF THIS WORK AND ITS DOCUMENTATION, EVEN IF TAMPERE UNIVERSITY OR ITS
LICENSORS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
TAMPERE UNIVERSITY AND ALL ITS LICENSORS SPECIFICALLY DISCLAIMS ANY
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE. THE WORK PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND
THE TAMPERE UNIVERSITY HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,
UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
"""
[docs]class Clip(core.Clip):
"""TAU Urban Acoustic Scenes 2022 Mobile Clip class
Args:
clip_id (str): id of the clip
Attributes:
audio (np.ndarray, float): path to the audio file
audio_path (str): path to the audio file
city (str): city were the audio signal was recorded
clip_id (str): clip id
identifier (str): the clip identifier
source_label (str): source label
split (str): subset the clip belongs to (for experiments):
development (fold1, fold2, fold3, fold4) or evaluation
tags (soundata.annotations.Tags): tag (label) of the clip + confidence
"""
def __init__(self, clip_id, data_home, dataset_name, index, metadata):
super().__init__(clip_id, data_home, dataset_name, index, metadata)
self.audio_path = self.get_path("audio")
@property
def audio(self) -> Optional[Tuple[np.ndarray, float]]:
"""The clip's audio
Returns:
* np.ndarray - audio signal
* float - sample rate
"""
return load_audio(self.audio_path)
@property
def split(self):
"""The clip's split.
Returns:
* str - subset the clip belongs to (for experiments): development (fold1, fold2, fold3, fold4) or evaluation
"""
return self._clip_metadata.get("split")
@property
def tags(self):
"""The clip's tags.
Returns:
* annotations.Tags - tag (label) of the clip + confidence
"""
scene_label = self._clip_metadata.get("scene_label")
if scene_label is None:
return None
else:
return annotations.Tags([scene_label], "open", np.array([1.0]))
@property
def city(self):
"""The clip's city.
Returns:
* str - city were the audio signal was recorded
"""
return self._clip_metadata.get("city")
@property
def source_label(self):
"""The clip's source label.
Returns:
* str - source label
"""
return self._clip_metadata.get("source_label")
@property
def identifier(self):
"""The clip's identifier.
Returns:
* str - clip identifier
"""
return self._clip_metadata.get("identifier")
[docs] def to_jams(self):
"""Get the clip's data in jams format
Returns:
jams.JAMS: the clip's data in jams format
"""
return jams_utils.jams_converter(
audio_path=self.audio_path, tags=self.tags, metadata=self._clip_metadata
)
[docs]@io.coerce_to_bytes_io
def load_audio(fhandle: BinaryIO, sr=None) -> Tuple[np.ndarray, float]:
"""Load a TAU Urban Acoustic Scenes 2022 Mobile audio file
Args:
fhandle (str or file-like): File-like object or path to audio file
sr (int or None): sample rate for loaded audio, None by default, which
uses the file's original sample rate of 44100 without resampling.
Returns:
* np.ndarray - the mono audio signal
* float - The sample rate of the audio file
"""
audio, sr = librosa.load(fhandle, sr=sr, mono=True)
return audio, sr
[docs]@core.docstring_inherit(core.Dataset)
class Dataset(core.Dataset):
"""The TAU Urban Acoustic Scenes 2022 Mobile dataset"""
def __init__(self, data_home=None):
super().__init__(
data_home,
name="tau2022uas_mobile",
clip_class=Clip,
bibtex=BIBTEX,
remotes=REMOTES,
license_info=LICENSE_INFO,
)
[docs] @core.copy_docs(load_audio)
def load_audio(self, *args, **kwargs):
return load_audio(*args, **kwargs)
@core.cached_property
def _metadata(self):
metadata_path = os.path.join(
self.data_home,
"TAU-urban-acoustic-scenes-2022-mobile-development",
"meta.csv",
)
splits = [
"2022.development.train",
"2022.development.evaluate",
"2023.evaluation",
]
metadata_index = {}
with open(metadata_path) as csv_file:
csv_reader = csv.reader(csv_file, delimiter="\t")
next(csv_reader)
for row in csv_reader:
file_name = os.path.basename(row[0])
clip_id = os.path.basename(file_name).replace(".wav", "")
scene_label = row[1]
identifier = row[2]
source_label = row[3]
city = identifier.split("-")[0]
metadata_index[clip_id] = {
"scene_label": scene_label,
"city": city,
"identifier": identifier,
"source_label": source_label,
}
for split in splits:
subset = split.split(".")[1]
year = split.split(".")[0]
evaluation_setup_path = (
"TAU-urban-acoustic-scenes-{}-mobile-{}/evaluation_setup".format(
year, subset
)
)
if subset == "development":
fold = split.split(".")[2]
evaluation_setup_file = os.path.join(
self.data_home, evaluation_setup_path, "fold1_{}.csv".format(fold)
)
else:
evaluation_setup_file = os.path.join(
self.data_home, evaluation_setup_path, "fold1_test.csv"
)
with open(evaluation_setup_file) as csv_file:
csv_reader = csv.reader(csv_file, delimiter="\t")
next(csv_reader)
for row in csv_reader:
print(row)
file_name = os.path.basename(row[0])
clip_id = os.path.basename(file_name).replace(".wav", "")
if subset != "development":
metadata_index[clip_id] = {
"scene_label": None,
"city": None,
"identifier": None,
"source_label": None,
}
print(metadata_index)
print(clip_id)
metadata_index[clip_id]["split"] = split
return metadata_index