Multiple speaker tracking using a microphone array by combining auditory processing and a gaussian mixture cardinalized probability hypothesis density filter

Plinge, Axel; Hauschildt, Daniel; Hennecke, Marius H.; Fink, Gernot A.

doi:10.1109/icassp.2011.5946986

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The detection and classification of acoustic events can be used for meeting and online lecture analysis and annotation [1]. For speech enhancement and speaker tracking [2] detecting non-speech events can improve the robustness in real world applications.…”

Section: Introductionmentioning

confidence: 99%

A Bag-of-Features approach to acoustic event detection

Plinge

Grzeszick

Fink

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The classification of acoustic events in indoor environments is an important task for many practical applications in smart environments. In this paper a novel approach for classifying acoustic events that is based on a Bag-of-Features approach is proposed. Mel and gammatone frequency cepstral coefficients that originate from psychoacoustic models are used as input features for the Bag-of representation. Rather than using a prior classification or segmentation step to eliminate silence and background noise, Bag-of-Features representations are learned for a background class. Supervised learning of codebooks and temporal coding are shown to improve the recognition rates. Three different databases are used for the experiments: the CLEAR sound event dataset, the D-CASE event dataset and a new set of smart room recordings.

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Section: Introductionmentioning

confidence: 99%

A Bag-of-Features approach to acoustic event detection

Plinge

Grzeszick

Fink

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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“…The first RFS based solution for multi-source acoustic tracking was proposed in [18]. Subsequent RFS-based solutions have been proposed for multi-source acoustic tracking with the Probability Hypothesis Density (PHD) filter [8], [19], [20], [21], the Cardinalized PHD filter [22], the Cardinality-Balanced Multi-Target Multi-Bernoulli filter [23], and the RFS Particle Filter [24].…”

Section: Introductionmentioning

confidence: 99%

Blind Separation for Multiple Moving Sources With Labeled Random Finite Sets

Ong

Nordholm

2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper proposes a novel solution for separating an unknown and time-varying number of moving acoustic sources in a blind setting using multiple microphone arrays. A standard steered-response power phase transform method is applied to extract source position measurements, which inevitably contain noise, false detections, missed detections, and are not labeled with the source identities. The imperfect measurements lead to the space-time permutation problem, as there is no information on how the measurements are associated to the sources in space, nor how the measurements are connected across time, if at all. To solve this problem, a labeled random finite set tracking framework is adopted to jointly estimate the source positions and their labels or identities. Based on these trajectory estimates, a corresponding set of time-varying generalized sidelobe cancellers is constructed to perform source separation. The overall algorithm operates in a block-wise or an online fashion and is scalable with the number of microphone arrays. The quality of the measurements, tracking, and separation, are evaluated respectively, with the OSPA metric, OSPA (2) metric, and ITU-T P.835 based listening tests, on both real-world and simulated data.

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Multi-speaker tracking using multiple distributed microphone arrays

Plinge

Fink

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Multiple speaker tracking using a microphone array by combining auditory processing and a gaussian mixture cardinalized probability hypothesis density filter

Cited by 5 publications

References 17 publications

A Bag-of-Features approach to acoustic event detection

A Bag-of-Features approach to acoustic event detection

Blind Separation for Multiple Moving Sources With Labeled Random Finite Sets

Multi-speaker tracking using multiple distributed microphone arrays

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