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EVERYSOUND SIGNED

Computational Analysis of Everyday Soundscapes

Total Cost €

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EC-Contrib. €

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Partnership

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 EVERYSOUND project word cloud

Explore the words cloud of the EVERYSOUND project. It provides you a very rough idea of what is the project "EVERYSOUND" about.

computational    mobile    give    separation    animal    music    individual    networks    reliably    park    internationally    accurately    vast    passing    quantities    sounds    easily    producing    automatically    place    multilayer    environments    sources    tools    audio    carry    awarded    events    street    expertise    amount    speech    size    speed    monitoring    intelligent    multiple    multimedia    unobtrusively    classification    breaking    recognize    overlapping    realistic    analyze    rural    urban    perceive    videos    pattern    hierarchical    soundscapes    home    youtube    biological    tackling    ground    uploaded    human    tens    transmitted    physical    sound    content    car    categorize    robots    cultural    joint    descriptions    recognition    natural    taxonomy    searching    hours    internet    everysound    approximate    distorted    environmental    form    area    source    phones    simultaneously    environment    algorithms    everyday    geographical    minute    social    captured    acoustic   

Project "EVERYSOUND" data sheet

The following table provides information about the project.

Coordinator
TTY-SAATIO 

There are not information about this coordinator. Please contact Fabio for more information, thanks.

 Coordinator Country Finland [FI]
 Total cost 1˙500˙000 €
 EC max contribution 1˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2014-STG
 Funding Scheme ERC-STG
 Starting year 2015
 Duration (year-month-day) from 2015-05-01   to  2020-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TAMPEREEN KORKEAKOULUSAATIO SR FI (TAMPERE) coordinator 1˙500˙000.00
2    TTY-SAATIO FI (TAMPERE) coordinator 0.00

Map

 Project objective

Sounds carry a large amount of information about our everyday environment and physical events that take place in it. For example, when a car is passing by, one can perceive the approximate size and speed of the car. Sound can easily and unobtrusively be captured e.g. by mobile phones and transmitted further – for example, tens of hours of audio is uploaded to the internet every minute e.g. in the form of YouTube videos. However, today's technology is not able to recognize individual sound sources in realistic soundscapes, where multiple sounds are present, often simultaneously, and distorted by the environment. The ground-breaking objective of EVERYSOUND is to develop computational methods which will automatically provide high-level descriptions of environmental sounds in realistic everyday soundscapes such as street, park, home, etc. This requires developing several novel methods, including joint source separation and robust pattern classification algorithms to reliably recognize multiple overlapping sounds, and a hierarchical multilayer taxonomy to accurately categorize everyday sounds. The methods are based on the applicant's internationally recognized and awarded expertise on source separation and robust pattern recognition in speech and music processing, which will allow now tackling the new and challenging research area of everyday sound recognition. The results of EVERYSOUND will enable searching for multimedia based on its audio content, which is not possible with today's technology. It will allow mobile devices, robots, and intelligent monitoring systems to recognize activities in their environments using acoustic information. Producing automatically descriptions of vast quantities of audio will give new tools for geographical, social, cultural, and biological studies to analyze sounds related to human, animal, and natural activity in urban and rural areas, as well as multimedia in social networks.

 Publications

year authors and title journal last update
List of publications.
2018 Gharib, Shayan; Drossos, Konstantinos; Çakir, Emre; Serdyuk, Dmitriy; Virtanen, Tuomas
Unsupervised adversarial domain adaptation for acoustic scene classification
published pages: , ISSN: , DOI:
Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events 2018 1 2020-03-10
2018 Paul Magron, Konstantinos Drossos, Stylianos Ioannis Mimilakis, Tuomas Virtanen
Reducing Interference with Phase Recovery in DNN-based Monaural Singing Voice Separation
published pages: 332-336, ISSN: , DOI: 10.21437/interspeech.2018-1845
Interspeech 2018 2020-03-10
2019 Sharath Adavanne, Archontis Politis, Joonas Nikunen, Tuomas Virtanen
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
published pages: 34-48, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2018.2885636
IEEE Journal of Selected Topics in Signal Processing 13/1 2020-03-10
2019 Adavanne, Sharath; Politis, Archontis; Virtanen, Tuomas
A multi-room reverberant dataset for sound event localization and detection
published pages: , ISSN: , DOI:
Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events 2019 1 2020-03-10
2019 Annamaria Mesaros, Toni Heittola, Tuomas Virtanen
Acoustic scene classification in DCASE 2019 Challenge: closed and open set classification and data mismatch setups
published pages: , ISSN: , DOI:
Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events 2019 2020-03-10
2017 Miroslav Malik, Sharath Adavanne, Konstantinos Drossos, Tuomas Virtanen, Dasa Ticha, Roman Jarina
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition
published pages: , ISSN: , DOI:
Proceedings of the 14th Sound and Music Computing Conference, 2017 2020-03-10
2018 Mesaros, Annamaria; Heittola, Toni; Virtanen, Tuomas
A multi-device dataset for urban acoustic scene classification
published pages: , ISSN: , DOI:
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop 1 2020-03-10
2019 Samuel Lipping, Konstantinos Drossos, Tuomas Virtanen
Crowdsourcing a Dataset of Audio Captions
published pages: , ISSN: , DOI:
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop 2020-03-10
2019 Sharath Adavanne, Archontis Politis, Tuomas Virtanen
Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network
published pages: , ISSN: , DOI:
Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events, 2019. 2020-03-10
2019 Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schluter, Shuo-Yiin Chang, Tara Sainath
Deep Learning for Audio Signal Processing
published pages: 206-219, ISSN: 1932-4553, DOI: 10.1109/jstsp.2019.2908700
IEEE Journal of Selected Topics in Signal Processing 13/2 2020-03-10
2019 Annamaria Mesaros, Aleksandr Diment, Benjamin Elizalde, Toni Heittola, Emmanuel Vincent, Bhiksha Raj, Tuomas Virtanen
Sound Event Detection in the DCASE 2017 Challenge
published pages: 992-1006, ISSN: 2329-9290, DOI: 10.1109/TASLP.2019.2907016
IEEE/ACM Transactions on Audio, Speech, and Language Processing 27/6 2020-03-10
2019 Konstantinos Drossos, Shayan Gharib, Paul Magron, Tuomas Virtanen
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
published pages: , ISSN: , DOI:
Proceedings of Workshop on Detection and Classification of Acoustic Scenes and Events 2019 2020-03-10
2017 Konstantinos Drossos, Stylianos Ioannis Mimilakis, Andreas Floros, Tuomas Virtanen, Gerald Schuller
Close Miking Empirical Practice Verification: A Source Separation Approach
published pages: , ISSN: , DOI:
In proceedings Audio Engineering Society 142th Convention 2020-03-10
2017 Annamaria Mesaros, Toni Heittola, Aleksandr Diment, Benjamin Elizalde, Ankit Shah, Emmanuel Vincent, Bhiksha Raj, and Tuomas Virtanen
DCASE 2017 challenge setup: tasks, datasets and baseline system
published pages: , ISSN: , DOI:
Proceedings of the Workshop on Detection and Classification of Sound Scenes and Events 2019-05-28
2018 Toni Heittola, Emre Çakır, Tuomas Virtanen
The Machine Learning Approach for Analysis of Sound Scenes and Events
published pages: 13-40, ISSN: , DOI: 10.1007/978-3-319-63450-0_2
Computational Analysis of Sound Scenes and Events 2019-05-27
2016 Tuomas Virtanen, Annamaria Mesaros, Toni Heittola, Mark D. Plumbley, Peter Foster, Emmanouil Benetos, and Mathieu Lagrange. (Eds.)
Proceedings of the detection and classification of acoustic scenes and events 2016 workshop (DCASE2016)
published pages: , ISSN: , DOI:
2019-05-28
2018 Annamaria Mesaros, Toni Heittola, Dan Ellis
Datasets and Evaluation
published pages: 147-179, ISSN: , DOI: 10.1007/978-3-319-63450-0_6
Computational Analysis of Sound Scenes and Events 2019-05-27
2017 Sharath Adavanne and Tuomas Virtanen
Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network
published pages: , ISSN: , DOI:
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017) 2019-05-27
2017 Tuomas Virtanen, Annamaria Mesaros, Toni Heittola, Aleksandr Diment, Emmanuel Vincent, Emmanouil Benetos, and Benjamin Martinez Elizalde. (Eds.)
Proceedings of the detection and classification of acoustic scenes and events 2017 workshop (DCASE2017)
published pages: , ISSN: , DOI:
2019-05-28
2018 Panu Maijala, Zhao Shuyang, Toni Heittola, Tuomas Virtanen
Environmental noise monitoring using source classification in sensors
published pages: 258-267, ISSN: 0003-682X, DOI: 10.1016/j.apacoust.2017.08.006
Applied Acoustics 129 2019-05-28
2017 Emre Cakir and Tuomas Virtanen
Convolutional Recurrent Neural Networks for Rare Sound Event Detection
published pages: , ISSN: , DOI:
Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events 2017 (DCASE 2017) 2019-05-27
2017 Emre Cakir, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
published pages: 1291-1303, ISSN: 2329-9290, DOI: 10.1109/taslp.2017.2690575
IEEE/ACM Transactions on Audio, Speech, and Language Processing 25/6 2019-05-27
2018 Annamaria Mesaros, Toni Heittola, Emmanouil Benetos, Peter Foster, Mathieu Lagrange, Tuomas Virtanen, Mark D. Plumbley
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
published pages: 379-393, ISSN: 2329-9290, DOI: 10.1109/taslp.2017.2778423
IEEE/ACM Transactions on Audio, Speech, and Language Processing 26/2 2019-05-28
2016 Annamaria Mesaros, Toni Heittola, Tuomas Virtanen
Metrics for Polyphonic Sound Event Detection
published pages: , ISSN: 2076-3417, DOI: 10.3390/app6060162
Applied Sciences 6 2019-05-28
2016 Konstantinos Drossos, Maximos Kaliakatsos-Papakostas, Andreas Floros, Tuomas Virtanen
On the Impact of The Semantic Content of Sound Events in Emotion Elicitation
published pages: 525-532, ISSN: 1549-4950, DOI: 10.17743/jaes.2016.0024
Journal of the Audio Engineering Society 64/7/8 2019-05-28

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