Acoustic Signal Processing and Scene Analysis for Socially Assistive Robots

Lead Research Organisation: Imperial College London
Department Name: Electrical and Electronic Engineering

Abstract

The interaction between users and a robot often takes place in busy environments in the presence of competing speakers and background noise sources such as televisions. The signals received at the microphones of the robot are hence a mixture of the signals from multiple sound sources, ambient noise, and reverberation due to reflections of sound waves. Thus, in order to focus on stimuli of interest, the robot has to learn and adapt to the acoustic environment.

The aim of this research is to provide robots and machines with the ability to understand and adapt to the surrounding acoustic environment. Acoustic scene analysis combines salient features from the observed audio signals in order to create situational awareness of the environment; Sound sources are detected, localised and identified, whilst acoustic properties of the room itself can be characterised. Using the information acquired by analysing the acoustic scene, a three-dimensional map of the environment is created, and can be used to identify sounds or recognise the intent of speech signals. Moreover, by moving within the environment, the robot can explore and learn about the acoustic properties of its surrounding.

However, many of the tasks required for analysis of the acoustic scene are jointly dependent. For example, localising the sources of sounds buried in noise and reverberation is a challenging problem. Sound source localisation can be improved by enhancing the signals of desired sources, such as human speakers, whilst suppressing interfering sources, such as a television. However, for source enhancement, desired and interfering sources must be spatially distinguished, hence requiring knowledge of the source directions.

The novel objective of this research is therefore to identify and exploit constructively the joint dependencies between the tasks required for acoustic scene analysis. To achieve this objective, the project will take advantage of the motion of the robot in order to look at uncertain events from different perspectives. Techniques will be developed to constructively exploit motion of the robot's arms by fusing microphones attached to the robot's limbs with microphone arrays installed in the robot head. Furthermore, approaches will be investigated that allow multiple robots to share their experience and knowledge about the acoustic environment.

The research will be conducted at Imperial College London, within the Department of Electrical and Electronic Engineering with academic advice from national, European, and international project partners at the University of Edinburgh, UK; International Audio Laboratories Erlangen, Germany; and Bar-Ilan University, Israel.

Planned Impact

Equipping machines with an understanding of the acoustic environment allows a robot to engage in verbal interactions with humans. The robot can also adapt to the environment, e.g., by stepping closer or increasing the volume. Moreover, abnormal sounds, such as the noise due to a household accident, can be detected and appropriate actions can be taken, e.g., calling an ambulance.

The potential impact of this project therefore spans across the sectors of healthcare, industry, academia, and the government.

From the perspective of healthcare, socially assistive robots that are capable of providing physical aid and non-physical interaction could facilitate low-cost assistance for the 1.6 million people in the UK who provide over 50 hours a week of unpaid care to family and friends, as well as patients who cannot rely on the support of relatives.

For the industry, the research results have the potential to impact a wide range of applications that rely on processing of sounds in realistic environments, including search-and-rescue technology, hearing aids, home entertainment systems, and automatic speech recognition systems.

For academia, the trans-disciplinary nature of the project has the potential to help bridge the gap between the fields of robotics, digital signal processing, and acoustics.

Finally, from a societal perspective, research targeting intuitive human-robot interaction promotes public acceptance of robots in everyday life, thereby supporting the government's initiative to become one of the world leading nations in the field of Robotics and Autonomous Systems, a market with estimated global economic impact of USD 1.7-4.5 trillion annually by 2025.
 
Description The work funded through this award focuses on acoustic scene mapping for robot audition, and is highly transdisciplinary, spanning across acoustic signal processing, machine learning and robotics. The following contributions were achieved:
1) We organised an IEEE-SPS data challenge and published an open-access data corpus and software framework for the evaluation of acoustic scene mapping algorithms in order to foster reproducible and comparable research in the field. (Relevant DOIs: 10.5281/zenodo.3630471, 10.1109/IWAENC.2018.8521288, doi.org/10.1109/SAM.2018.8448644)
2) We pioneered Acoustic Simultaneous Localization and Mapping (SLAM) for robot audition. The proposed approach equips robots and autonomous machines with the spatial awareness required to understand and interact with sound sources in everyday environments. (Relevant DOIs: 10.1109/TASLP.2018.2828321, 10.1109/TSP.2017.2775590)
3) We developed a novel approach for acoustic scene mapping in smart environments that allows the robot to collaborate and share information with other devices in acoustic sensor networks, such as mobile phones, home assistants, or hearing aids. (Relevant DOI: 10.1109/LSP.2018.2849579)
4) We proposed new algorithms for the separation and enhancement of audio signals that are adversely affected by interference, noise and reverberation. The proposed algorithms exploit the correlations in the harmonic structure of speech signals. By tracking the voice pitch of multiple, the onsets and endpoints of multiple, overlapping speech signals can be detected in order to answer the question "Who speaks when?". Furthermore, we showed that the lack of correlation between desired source signals and any interfering noise signals (such as background babble, or fan noise) can be exploited constructively for the enhancement of the speech signals. (Relevant DOIs: 10.1109/WASPAA.2019.8937235, 10.1109/ICASSP.2019.8682924, 10.1109/WASPAA.2019.8937185)
5) We proposed a novel approach to acoustic environment classification based on deep neural networks (Relevant DOI: 10.23919/EUSIPCO.2017.8081293, 10.1109/ICASSP.2017.7952257)
Exploitation Route The research funded by this award provided the underpinning theoretical framework for acoustic scene mapping. Current and future research build upon this framework to ensure long-term, scalable machine autonomy.

Academic research will build upon the framework developed for this funding in order to develop novel, data-driven approaches to machine listening. Non-academic routes will investigate applications for commercial and societal impact, including autonomous vehicles (transport); unmanned vehicles (aerospace & defence); immersive environments (retail); assistive robotics and hearing aids (healthcare); as well as pipeline fault detection and underwater robotics (environment).
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Environment,Healthcare,Retail,Transport

 
Title Software for performance evaluation and benchmarking of algorithms for acoustic source localization and tracking for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA) 
Description A MATLAB framework was developed that allows researchers to objectively evaluate and benchmark their algorithms against state-of-the-art approaches in acoustic source localization and tracking. The framework was released as open-source code with the development database for the IEEE-AASP Challenge on acoustic source localization and tracking (LOCATA)/ 
Type Of Material Technology assay or reagent 
Year Produced 2020 
Provided To Others? Yes  
Impact As of 14 March 2018, i.e., 30 days after release of the data, 61 researchers have registered for download of the data. Registered users indicated fields of expertise in audio signal processing, acoustics, as well as robotics. A measurable impact of the LOCATA challenge, its data corpus and benchmarking framework will be available after completion and evaluation of the Challenge in September 2018. 
URL https://github.com/cevers/sap_locata_eval
 
Title Software for using the LOCATA Challenge dataset 
Description Open-access Matlab framework for reading data in the LOCATA Challenge dataset. This framework allows users to apply their own algorithms to the dataset and write the results to file. 
Type Of Material Improvements to research infrastructure 
Year Produced 2020 
Provided To Others? Yes  
Impact As of 14 March 2018, i.e., 30 days after release of the data, 61 researchers have registered for download of the data. Registered users indicated fields of expertise in audio signal processing, acoustics, as well as robotics. A measurable impact of the LOCATA challenge, its data corpus and benchmarking framework will be available after completion and evaluation of the Challenge in September 2018. 
URL https://github.com/cevers/sap_locata_io
 
Title Open-access data corpus for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA) 
Description The challenge of sound source localization in acoustically complex environments has attracted widespread attention in the research community in recent years. Source localization approaches in the literature range from single-sensor to multi-sensor and distributed arrays, based on features including, for example, Time Delays of Arrival, Direction of Arrival, or even audio spectrograms. Nevertheless, despite the significant impact of sound source localization approaches, a comprehensive, objective benchmarking campaign of state-of-the-art algorithms is to date unavailable. The IEEE AASP challenge on acoustic source LOCalization And TrAcking (LOCATA) aims at providing researchers in source localization with a framework to objectively benchmark results against competing algorithms using a common, publically released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment. The challenge data was released in February 2018 and will complete in September 2018. Results will be disseminated at a dedicated satellite workshop, held during the International Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan, in 17-20 Sept 2018. A detailed description of the corpus can be found in the documentation, available for download at www.locata-challenge.org. A summary is submitted as a conference paper (see Loellmann et al, "The LOCATA Challenge Data Corpus for Acoustic Localization and Tracking"). 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
Impact The data was released on 16 February 2018. As of 14 March 2018 - less than a month after its release - 61 researchers registered for download of the dataset, indicating rapid adoption of the corpus by the community. The LOCATA data corpus is the first corpus of its kind, providing researchers and practitioners with acoustic recordings and highly accurate ground truth positional information of the acoustic sensors and sound sources within a realistic acoustic environment. It is therefore envisaged that the corpus will be utilized by practitioners in the long-term for objective evaluation and benchmarking within various areas including acoustic source localization, tracking, source separation and diarization, Simultaneous Localization and Mapping as well as motion and path planning. 
URL http://doi.org/10.5281/zenodo.3630471
 
Description Audio Tracking using Variational Expectation-Maximization (VEM) 
Organisation The National Institute for Research in Computer Science and Control (INRIA)
Country France 
Sector Public 
PI Contribution N/A
Collaborator Contribution N/A
Impact The following journal article was accepted for publication, subject to minor revisions: Y. Ban, X. Alameda-Pineda, C. Evers, R. Horaud, "Tracking Multiple Audio Sources With the von Mises Distribution and Variational EM", in Signal Processing Letters.
Start Year 2018
 
Description Bayesian track-before-detect with Bar-Ilan University, Israel 
Organisation Bar-Ilan University
Country Israel 
Sector Academic/University 
PI Contribution Lead researcher of the collaboration. Provision of expertise in Bayesian inference, acoustic source tracking, as well as in robotics for acoustic sensor arrays installed on autonomous, moving platforms.
Collaborator Contribution Provision of expertise in acoustic source localization. Provision of access to specialized equipment, including a unique audio laboratory with controlled acoustics.
Impact During the research, we proved that acoustic sources can be directly tracked from acoustic signals, without the need for a pre-processing stage for source localization and / or detection. The research is placed at the intersection between acoustics, signal processing, and robotics and is therefore highly multidisciplinary. A journal article is currently in preparation.
Start Year 2017
 
Description Distributed acoustic source localization and tracking 
Organisation Bar-Ilan University
Country Israel 
Sector Academic/University 
PI Contribution Lead researcher of the collaboration. Provision of expertise in Bayesian inference, as well as in robotics for acoustic sensor arrays installed on autonomous, moving platforms. Dr Christine Evers visited AudioLabs Erlangen in June 2017 for 3 weeks in order to work with the collaborators, Prof. Emanuel Habets (Friedrich-Alexander University) and Prof. Sharon Gannot (Bar-Ilan University).
Collaborator Contribution Provision of expertise in spatial audio (Friedrich-Alexander University) and distributed sensing (Bar-Ilan University). Provision of access to simulators and methods for estimation of acoustic features, including estimators of the direction-of-arrival of sound sources and coherent-to-diffuse ratio at an acoustic sensor.
Impact Submission of journal paper: C. Evers, E. A. P. Habets, S. Gannot, and P. A. Naylor, "DoA Reliability for Distributed Acoustic Tracking", submitted to IEEE Signal Processing Letters, Mar. 2018. Disciplines: Signal Processing, Acoustics
Start Year 2017
 
Description Distributed acoustic source localization and tracking 
Organisation Friedrich-Alexander University Erlangen-Nuremberg
Country Germany 
Sector Academic/University 
PI Contribution Lead researcher of the collaboration. Provision of expertise in Bayesian inference, as well as in robotics for acoustic sensor arrays installed on autonomous, moving platforms. Dr Christine Evers visited AudioLabs Erlangen in June 2017 for 3 weeks in order to work with the collaborators, Prof. Emanuel Habets (Friedrich-Alexander University) and Prof. Sharon Gannot (Bar-Ilan University).
Collaborator Contribution Provision of expertise in spatial audio (Friedrich-Alexander University) and distributed sensing (Bar-Ilan University). Provision of access to simulators and methods for estimation of acoustic features, including estimators of the direction-of-arrival of sound sources and coherent-to-diffuse ratio at an acoustic sensor.
Impact Submission of journal paper: C. Evers, E. A. P. Habets, S. Gannot, and P. A. Naylor, "DoA Reliability for Distributed Acoustic Tracking", submitted to IEEE Signal Processing Letters, Mar. 2018. Disciplines: Signal Processing, Acoustics
Start Year 2017
 
Description IEEE-AASP Challenge on acoustic source localization and tracking (LOCATA) 
Organisation Friedrich-Alexander University Erlangen-Nuremberg
Country Germany 
Sector Academic/University 
PI Contribution Research visit at Humboldt-University Berlin in January 2017 for the collaborative measurement campaign of the LOCATA data corpus. Provision of expertise in acoustic signal processing for processing of the data, and creation of the benchmark framework for challenge participants. Provision of code for benchmark approaches for acoustic source localization and tracking, used as baseline methods for evaluation of the results. Provision of specialized spherical microphone arrays, including pseudospherical bespoke robot head and spherical mh acoustics eigenmike, for recordings.
Collaborator Contribution Friedrich-Alexander University: Lead of project - Provision of specialized equipment, including hearing aid dummies, dummy head, linear microphone array, and audio interfaces, for recordings. Provision of expertise in audio signal processing for processing of audio data, and creation of code for challenge participants. Humboldt-University Berlin: Provision of specialized equipment (optical tracking system) and facillities for the measurement of ground-truth data of all sound-source and microphone positions, orientations, and velocities. Provision of expert to process the data acquired using the optical tracking system.
Impact Release of open-access data corpus, available at www.locata-challenge.org. As of 14 March 2018, 30 days after the release, 61 researchers registered for download of the data. The challenge impacts on research across the fields of acoustics, sensor array and multichannel signal processing, audio signal processing, machine learning and robotics. To describe the data corpus, a conference paper was submitted in March 2018: H. Löllmann, C. Evers, A. Schmidt, H. Mellmann, H. Barfuss, P. A. Naylor, and W. Kellermann, "The LOCATA Challenge Data Corpus for Acoustic Localization and Tracking", submitted to IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Mar. 2018. To disseminate the challenge results, two special sessions at international conferences to be held in 2018 are accepted: 1) Special Session on "Localization for Audio Applications," to be held at the IEEE Sensor Array and Multichannel Signal Processing Workshop, Sheffield, UK; 2) Special Session for the dissemination of the LOCATA challenge results: "LOCATA Challenge Workshop," to be held at Intl. Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan.
Start Year 2017
 
Description IEEE-AASP Challenge on acoustic source localization and tracking (LOCATA) 
Organisation Humboldt University of Berlin
Country Germany 
Sector Academic/University 
PI Contribution Research visit at Humboldt-University Berlin in January 2017 for the collaborative measurement campaign of the LOCATA data corpus. Provision of expertise in acoustic signal processing for processing of the data, and creation of the benchmark framework for challenge participants. Provision of code for benchmark approaches for acoustic source localization and tracking, used as baseline methods for evaluation of the results. Provision of specialized spherical microphone arrays, including pseudospherical bespoke robot head and spherical mh acoustics eigenmike, for recordings.
Collaborator Contribution Friedrich-Alexander University: Lead of project - Provision of specialized equipment, including hearing aid dummies, dummy head, linear microphone array, and audio interfaces, for recordings. Provision of expertise in audio signal processing for processing of audio data, and creation of code for challenge participants. Humboldt-University Berlin: Provision of specialized equipment (optical tracking system) and facillities for the measurement of ground-truth data of all sound-source and microphone positions, orientations, and velocities. Provision of expert to process the data acquired using the optical tracking system.
Impact Release of open-access data corpus, available at www.locata-challenge.org. As of 14 March 2018, 30 days after the release, 61 researchers registered for download of the data. The challenge impacts on research across the fields of acoustics, sensor array and multichannel signal processing, audio signal processing, machine learning and robotics. To describe the data corpus, a conference paper was submitted in March 2018: H. Löllmann, C. Evers, A. Schmidt, H. Mellmann, H. Barfuss, P. A. Naylor, and W. Kellermann, "The LOCATA Challenge Data Corpus for Acoustic Localization and Tracking", submitted to IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Mar. 2018. To disseminate the challenge results, two special sessions at international conferences to be held in 2018 are accepted: 1) Special Session on "Localization for Audio Applications," to be held at the IEEE Sensor Array and Multichannel Signal Processing Workshop, Sheffield, UK; 2) Special Session for the dissemination of the LOCATA challenge results: "LOCATA Challenge Workshop," to be held at Intl. Workshop on Acoustic Signal Enhancement (IWAENC), Tokyo, Japan.
Start Year 2017
 
Title Acoustic Simultaneous Localization and Mapping (aSLAM) 
Description A novel approach that simultaneously estimates the position and orientation of a sensor installed on a moving platform, such as a robot, whilst jointly estimating the positions of nearby sound sources. In this work, we proposed a novel approach, named acoustic SLAM (aSLAM), to map the positions of sound sources passively, and simultaneously localize a moving observer in realistic acoustic environments. aSLAM is based on the theoretical foundations of Random Finite Sets (RFSs) in order to map multiple, intermittently active sources, such as human talkers, subject to erroneous, false and missing DoA estimates. Moreover, to avoid the active emission of intrusive sound stimuli, aSLAM passively infers the 3D Cartesian source positions from the 2D DoA estimates, by exploiting constructively the spatiotemporal diversity of the observer for probabilistic source triangulation. aSLAM is based on GEM-SLAM, detailed under Software & Technical Products "New/Improved Technique/Technology - Optimized Self-Localization for SLAM (2018)". The following journal article is currently under review: C. Evers and P. A. Naylor, "Acoustic SLAM," in review, IEEE Trans. Audio, Speech and Language Processing, submitted Oct. 2017. 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2018 
Impact This is one of two papers that pioneer Acoustic Simultaneous Localization and Mapping (SLAM) for robot audition. The research is highly transdisciplinary, spanning across acoustic signal processing, machine learning and robotics. As the culmination of a well-cited conference paper (10.1109/ICASSP.2016.7471626), this article was published in the top journal for Acoustic Signal Processing (IF 3.531). The paper received over 2370 full-text views on IEEEXplore (Feb'20). The cutting-edge idea led to two keynote speeches (http://hscma2017.org/KeynoteSpeakers.asp, http://mi.eng.cam.ac.uk/UKSpeech2017/keynotes.html), several invited talks, international collaborations (Audiolabs Erlangen, Germany; Bar-Ilan University, Israel; INRIA, France), and the IEEE-SPS LOCATA Challenge (https://doi.org/10.5281/zenodo.3630471). 
 
Title DoA Reliability for Acoustic Source Tracking 
Description A novel approach for acoustic source tracking in distributed sensor networks that models the reliability of direction-of-arrival (DoA) estimates in order to distinguish accurate DoAs obtained at microphone arrays nearby a source from uncertain DoAs acquired at distant arrays. This paper proposes to incorporate the coherent-to-diffuse ratio as a DoA reliability for distributed acoustic tracking. The novel contribution is a tracking algorithm that probabilistically triangulates the Cartesian source positions by fusing the DoA estimates from all nodes within the network. The following journal article was submitted for publication and is currently under review: C. Evers, E. A. P. Habets, S. Gannot, and P. A. Naylor, "DoA Reliability for Distributed Acoustic Tracking", submitted to IEEE Signal Processing Letters, Mar. 2018. 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2018 
Impact This paper is the outcome of an international collaboration with Audiolabs Erlangen, Germany, and Bar Ilan University, Israel, on data fusion in acoustic sensor networks. The paper provides the fundamental probabilistic framework for dealing with rotations and translations due to the spatial diversity of network nodes. The paper was published in a highly cited journal in Signal Processing (IF 3.268), targeted at fast publication of cutting edge research. Due to the page limitation, thorough derivations are provided in the peer-reviewed supplementary materials (https://ieeexplore.ieee.org/ielx7/97/8411353/8392398/supplementary_material.pdf?tp=&arnumber=8392398). The results of this paper were extended to multi-source tracking in 10.1109/LSP.2019.2908376. 
 
Title Multi-Source Tracking using Variational Expectation-Maximization 
Description This work proposes a novel variational expectation-maximisation approach for the tracking of multiple, moving human talkers in realistic acoustic environments. 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2019 
Impact This paper is the result of an international collaboration with INRIA, France. The work in this paper is the culmination of a conference paper (10.1109/HSCMA.2017.7895564), which received the best-paper award at an international conference (https://team.inria.fr/perception/hscma17-award/). This paper was published in a highly cited journal in Signal Processing (IF 3.268), targeted at fast publication of cutting-edge ideas. Due to the limitation of the paper length, thorough and rigorous derivations, as well as open-access code and video demonstrations are provided in the supplementary materials (https://team.inria.fr/perception/research/audiotrack-vonm/). 
 
Title Optimized Self-Localization for SLAM 
Description A novel approach that self-localizes the position and orientation of a sensor, whilst jointly tracking the time-varying positions of nearby dynamic objects. Existing approach to Simultaneous Localization and Mapping (SLAM) are designed for visual and optical sensors. The aim of this work is to provide a theoretical framework for SLAM that is suitable for sensors that facilitate perception beyond vision but are not yet conventionally used for SLAM, such as acoustic microphone arrays [11], [12]. Since many non-conventional sensors are deployed in environments where the positions of the observer and the objects are highly time-varying, e.g., underwater, the objective of this work is the development of an approach that is specifically designed for dynamic environments. For robust SLAM performance in dynamic and uncertain environments, we proposed a novel approach, called GEneralized Motion (GEM)-SLAM, that fuses the knowledge inferred from feature mapping with reports of the observer motion. 
Type Of Technology New/Improved Technique/Technology 
Year Produced 2018 
Impact This is one of two papers that pioneer Acoustic Simultaneous Localization and Mapping (SLAM) for robot audition. This paper provides the underpinning, theoretical framework for the optimal fusion of sensor signals and inertial measurements in uncertain, dynamic scenes. The paper was published in the top journal in Signal Processing (journal IF 5.23). The paper received over 1370 full-text views on IEEEXplore (Feb 2020) and led to two keynote speeches (http://hscma2017.org/KeynoteSpeakers.asp, http://mi.eng.cam.ac.uk/UKSpeech2017/keynotes.html), several invited talks, international collaborations (Audiolabs Erlangen, Germany; Bar-Ilan University, Israel; INRIA, France), and the IEEE-SPS LOCATA Challenge (https://doi.org/10.5281/zenodo.3630471). 
 
Description Organization of satellite working during the 2018 International Workshop on Acoustic Signal Enhancement for dissemination of the outcomes of the IEEE-AASP Challenge on acoustic source Localization and Tracking (LOCATA), held 17-20 September 2018, Tokyo, Japan 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact A workshop for the announcement and dissemination of the IEEE-AASP LOCATA Challenge on acoustic source localization and tracking was organized as a satellite workshop to the "International Workshop on Acoustic Signal Enhancement" (IWAENC) in Tokyo in September 2018. Approximately 80 participants from research and industry attended the workshop, which sparked questions about future directions and open research questions in the field of acoustic source localization and tracking. Challenge participants participants reported that the data corpus published for the LOCATA challenge provided them with a deeper understanding of the challenges in practical scenarios. As a consequence, challenge participants as well as workshop participants reported a change in view about the importance of the field of acoustic source localziation and tracking and the challenges and future directions involved.
Year(s) Of Engagement Activity 2018
URL http://www.iwaenc2018.org
 
Description Organization of special session during the 2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) on "Localization for audio applications", held 8-11 July 2018, Sheffield, UK 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Smart and autonomous systems, such as home assistants, mobile phones, and assistive robots, rely on robust multichannel audio processing in order to interact intuitively with humans in the acoustic environment. In order to detect, engage with and focus on human talkers, sound source localization is crucial for human-machine interaction. However, in realistic acoustic scenarios, localization algorithms are subject to the adverse effects of reverberation, noise, and interference. Moreover, acoustic sensor arrays are often installed on mobile platforms, for example for humanoid robots or hearing aids. The motion of the sensor array as well as the human sources in the environment therefore lead to highly dynamic scenarios. The aim of the proposed Special Session on 'Localization for Audio Applications' is to present recent approaches and methods for sound source localization that address the practical challenges encountered in everyday environments. Presenters and listeners in this session will benefit from a broad selection of state-of-the-art approaches and timely applications.
Year(s) Of Engagement Activity 2018
URL http://www.sam2018.org