Microphone array signal processing for robot audition

Löllmann, Heinrich W.; Moore, AlastairH.; Naylor, Patrick A.; Rafaely, Boaz; Horaud, Radu; Mazel, Alexandre; Kellermann, Walter

doi:10.1109/hscma.2017.7895560

Cited by 12 publications

(7 citation statements)

References 43 publications

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“…Algorithm 2 generates samples of the source signal X i (k) and the additive noise signals N i (k) and composes the microphone signals Y i (k) according to (2). The algorithm finishes with the feature extraction according to (4). In practice, the algorithmic steps can be implemented efficiently in vectorized form.…”

Section: Algorithmmentioning

confidence: 99%

“…Sound source localization is a crucial task in array signal processing that is used in applications like sound source separation [1], speech recognition [2], camera surveillance [3], and robot audition [4]. A special case of source localization is direction of arrival (DOA) estimation, which aims at determining the angular position of a source relative to a sensor array.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Training Data Generation for Phase-Based DOA Estimation

Hubner

Mack

Habets

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Deep learning (DL) based direction of arrival (DOA) estimation is an active research topic and currently represents the state-of-the-art. Usually, DL-based DOA estimators are trained with recorded data or computationally expensive generated data. Both data types require significant storage and excessive time to, respectively, record or generate. We propose a low complexity online data generation method to train DL models with a phase-based feature input. The data generation method models the phases of the microphone signals in the frequency domain by employing a deterministic model for the direct path and a statistical model for the late reverberation of the room transfer function. By an evaluation using data from measured room impulse responses, we demonstrate that a model trained with the proposed training data generation method performs comparably to models trained with data generated based on the source-image method.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Training Data Generation for Phase-Based DOA Estimation

Hubner

Mack

Habets

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Author details 1 Audio Analysis Lab, CREATE, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark. 2 Bar-Ilan University, 5290002 Ramat-Gan, Israel.…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

“…During the past decade, there has been an increased research interest in robot and drone audition [1][2][3]. Hearing capabilities enable robots to understand and interact with humans [4].…”

Section: Introductionmentioning

confidence: 99%

Estimation of acoustic echoes using expectation-maximization methods

Saqib

Gannot

Jensen

2020

J AUDIO SPEECH MUSIC PROC.

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Estimation problems like room geometry estimation and localization of acoustic reflectors are of great interest and importance in robot and drone audition. Several methods for tackling these problems exist, but most of them rely on information about times-of-arrival (TOAs) of the acoustic echoes. These need to be estimated in practice, which is a difficult problem in itself, especially in robot applications which are characterized by high ego-noise. Moreover, even if TOAs are successfully extracted, the difficult problem of echolabeling needs to be solved. In this paper, we propose multiple expectation-maximization (EM) methods, for jointly estimating the TOAs and directions-of-arrival (DOA) of the echoes, with a uniform circular array (UCA) and a loudspeaker in its center for probing the environment. The different methods are derived to be optimal under different noise conditions. The experimental results show that the proposed methods outperform existing methods in terms of estimation accuracy in noisy conditions. For example, it can provide accurate estimates at SNR of 10 dB lower compared to TOA extraction from room impulse responses, which is often used. Furthermore, the results confirm that the proposed methods can account for scenarios with colored noise or faulty microphones. Finally, we show the applicability of the proposed methods in mapping of an indoor environment.

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“…using particle filters [133,40] and probability hypothesis density (PHD) filters [41]. Two recent European projects, the "Ears" 3 project and the "Two!Ears" 4 project, explored new algorithms for enhancing the auditory capabilities of humanoid robots [85] and link them with decision and action [19].…”

Section: Moving Sources and Sensorsmentioning

confidence: 99%

Audio source separation into the wild

Girin

Gannot

2019

Multimodal Behavior Analysis in the Wild

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This review chapter is dedicated to multichannel audio source separation in real-life environment. We explore some of the major achievements in the field and discuss some of the remaining challenges. We will explore several important practical scenarios, e.g. moving sources and/or microphones, varying number of sources and sensors, high reverberation levels, spatially diffuse sources, and synchronization problems. Several applications such as smart assistants, cellular phones, hearing aids and robots, will be discussed. Our perspectives on the future of the field will be given as concluding remarks of this chapter.

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Microphone array signal processing for robot audition

Cited by 12 publications

References 43 publications

Efficient Training Data Generation for Phase-Based DOA Estimation

Efficient Training Data Generation for Phase-Based DOA Estimation

Estimation of acoustic echoes using expectation-maximization methods

Audio source separation into the wild

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