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Jean-Marc Valin

Publications and source records attributed to Jean-Marc Valin.

At least 55 records · Page 3Linked to original sources

Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter

We propose a system that gives a mobile robot the ability to separate simultaneous sound sources. A microphone array is used along with a real-time dedicated implementation of Geometric Source Separation and a post-filter that gives us a further reduction of interferences from other sources. We present results and comparisons for separation of multiple non-stationary speech sources combined with noise sources. The main advantage of our approach for mobile robots resides in the fact that both the frequency-domain Geometric Source Separation algorithm and the post-filter are able to adapt rapidly to new sources and non-stationarity. Separation results are presented for three simultaneous interfering speakers in the presence of noise. A reduction of log spectral distortion (LSD) and increase of signal-to-noise ratio (SNR) of approximately 10 dB and 14 dB are observed.

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Low-Complexity Iterative Sinusoidal Parameter Estimation

Sinusoidal parameter estimation is a computationally-intensive task, which can pose problems for real-time implementations. In this paper, we propose a low-complexity iterative method for estimating sinusoidal parameters that is based on the linearisation of the model around an initial frequency estimate. We show that for N sinusoids in a frame of length L, the proposed method has a complexity of O(LN), which is significantly less than the matching pursuits method. Furthermore, the proposed method is shown to be more accurate than the matching pursuits and time frequency reassignment methods in our experiments.

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Improved Noise Weighting in CELP Coding of Speech - Applying the Vorbis Psychoacoustic Model To Speex

One key aspect of the CELP algorithm is that it shapes the coding noise using a simple, yet effective, weighting filter. In this paper, we improve the noise shaping of CELP using a more modern psychoacoustic model. This has the significant advantage of improving the quality of an existing codec without the need to change the bit-stream. More specifically, we improve the Speex CELP codec by using the psychoacoustic model used in the Vorbis audio codec. The results show a significant increase in quality, especially at high bit-rates, where the improvement is equivalent to a 20% reduction in bit-rate. The technique itself is not specific to Speex and could be applied to other CELP codecs.

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Speex: A Free Codec For Free Speech

The Speex project has been started in 2002 to address the need for a free, open-source speech codec. Speex is based on the Code Excited Linear Prediction (CELP) algorithm and, unlike the previously existing Vorbis codec, is optimised for transmitting speech for low latency communication over an unreliable packet network. This paper presents an overview of Speex, the technology involved in it and how it can be used in applications. The most recent developments in Speex, such as the fixed-point port, acoustic echo cancellation and noise suppression are also addressed.

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Perceptually-Motivated Nonlinear Channel Decorrelation For Stereo Acoustic Echo Cancellation

Acoustic echo cancellation with stereo signals is generally an under-determined problem because of the high coherence between the left and right channels. In this paper, we present a novel method of significantly reducing inter-channel coherence without affecting the audio quality. Our work takes into account psychoacoustic masking and binaural auditory cues. The proposed non-linear processing combines a shaped comb-allpass (SCAL) filter with the injection of psychoacoustically masked noise. We show that the proposed method performs significantly better than other known methods for reducing inter-channel coherence.

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Robust 3D Localization and Tracking of Sound Sources Using Beamforming and Particle Filtering

In this paper we present a new robust sound source localization and tracking method using an array of eight microphones (US patent pending) . The method uses a steered beamformer based on the reliability-weighted phase transform (RWPHAT) along with a particle filter-based tracking algorithm. The proposed system is able to estimate both the direction and the distance of the sources. In a videoconferencing context, the direction was estimated with an accuracy better than one degree while the distance was accurate within 10% RMS. Tracking of up to three simultaneous moving speakers is demonstrated in a noisy environment.

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A New Robust Frequency Domain Echo Canceller With Closed-Loop Learning Rate Adaptation

One of the main difficulties in echo cancellation is the fact that the learning rate needs to vary according to conditions such as double-talk and echo path change. Several methods have been proposed to vary the learning. In this paper we propose a new closed-loop method where the learning rate is proportional to a misalignment parameter, which is in turn estimated based on a gradient adaptive approach. The method is presented in the context of a multidelay block frequency domain (MDF) echo canceller. We demonstrate that the proposed algorithm outperforms current popular double-talk detection techniques by up to 6 dB.

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Localization of Simultaneous Moving Sound Sources for Mobile Robot Using a Frequency-Domain Steered Beamformer Approach

Mobile robots in real-life settings would benefit from being able to localize sound sources. Such a capability can nicely complement vision to help localize a person or an interesting event in the environment, and also to provide enhanced processing for other capabilities such as speech recognition. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a probabilistic post-processor. Results show that a mobile robot can localize in real time multiple moving sources of different types over a range of 5 meters with a response time of 200 ms.

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Extension spectrale d'un signal de parole de la bande téléphonique à la bande AM

This document proposes a bandwidth extension system producing a wideband signal from a narrowband speech signal. The extension is performed independently for high and low frequencies. High-frequency extension uses the excitation-filter model. Extension of the excitation is performed in the time domain using a non-linear function, while the spectral envelope is extended in the cepstral domain using a multi-layer perceptron. Low-band extension is based on the sinusoidal model. The amplitude of sinusoids is also estimated using a multi-layer perceptron. The results show that the sound quality after extension is higher than that of narrowband speech, with a significant variation across listeners. Some of the techniques, including excitation extension, are of interest in the field of speech coding. ----- Le présent mémoire propose un système d'extension de la bande permettant de produire un signal en bande AM à partir d'un signal de parole en bande téléphonique. L'extension est effectuée de façon indépendante pour les hautes fréquences et les basses fréquences. L'extension des hautes fréquences utilise le modèle filtre-excitation. L'extension de l'excitation est réalisée dans le domaine temporel par une fonction non linéaire, alors que l'extension de l'enveloppe spectrale s'effectue dans le domaine cepstral par un perceptron multi-couches. L'extension de la bande basse utilise le modèle sinusoïdal. L'amplitude des sinusoïdes est aussi estimée par un perceptron multi-couches. Les résultats obtenus montrent que la qualité sonore après extension est supérieure à celle de la bande téléphonique, avec une importante différence entre les auditeurs. Certaines techniques développées, dont l'extension de l'excitation, présentent un certain intérêt pour le domaine du codage de la parole.

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Robust Sound Source Localization Using a Microphone Array on a Mobile Robot

The hearing sense on a mobile robot is important because it is omnidirectional and it does not require direct line-of-sight with the sound source. Such capabilities can nicely complement vision to help localize a person or an interesting event in the environment. To do so the robot auditory system must be able to work in noisy, unknown and diverse environmental conditions. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on time delay of arrival estimation. Results show that a mobile robot can localize in real time different types of sound sources over a range of 3 meters and with a precision of 3 degrees.

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Bandwidth Extension of Narrowband Speech for Low Bit-Rate Wideband Coding

Wireless telephone speech is usually limited to the 300-3400 Hz band, which reduces its quality. There is thus a growing demand for wideband speech systems that transmit from 50 Hz to 8000 Hz. This paper presents an algorithm to generate wideband speech from narrowband speech using as low as 500 bits/s of side information. The 50-300 Hz band is predicted from the narrowband signal. A source-excitation model is used for the 3400-8000 Hz band, where the excitation is extrapolated at the receiver, and the spectral envelope is transmitted. Though some artifacts are present, the resulting wideband speech has enhanced quality compared to narrowband speech.

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Robust Localization and Tracking of Simultaneous Moving Sound Sources Using Beamforming and Particle Filtering

Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 meters. These new capabilities allow a mobile robot to interact using more natural means with people in real life settings.

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Interference-Normalised Least Mean Square Algorithm

An interference-normalised least mean square (INLMS) algorithm for robust adaptive filtering is proposed. The INLMS algorithm extends the gradient-adaptive learning rate approach to the case where the signals are non-stationary. In particular, we show that the INLMS algorithm can work even for highly non-stationary interference signals, where previous gradient-adaptive learning rate algorithms fail.

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On Adjusting the Learning Rate in Frequency Domain Echo Cancellation With Double-Talk

One of the main difficulties in echo cancellation is the fact that the learning rate needs to vary according to conditions such as double-talk and echo path change. In this paper we propose a new method of varying the learning rate of a frequency-domain echo canceller. This method is based on the derivation of the optimal learning rate of the NLMS algorithm in the presence of noise. The method is evaluated in conjunction with the multidelay block frequency domain (MDF) adaptive filter. We demonstrate that it performs better than current double-talk detection techniques and is simple to implement.

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Auditory System for a Mobile Robot

In this thesis, we propose an artificial auditory system that gives a robot the ability to locate and track sounds, as well as to separate simultaneous sound sources and recognising simultaneous speech. We demonstrate that it is possible to implement these capabilities using an array of microphones, without trying to imitate the human auditory system. The sound source localisation and tracking algorithm uses a steered beamformer to locate sources, which are then tracked using a multi-source particle filter. Separation of simultaneous sound sources is achieved using a variant of the Geometric Source Separation (GSS) algorithm, combined with a multi-source post-filter that further reduces noise, interference and reverberation. Speech recognition is performed on separated sources, either directly or by using Missing Feature Theory (MFT) to estimate the reliability of the speech features. The results obtained show that it is possible to track up to four simultaneous sound sources, even in noisy and reverberant environments. Real-time control of the robot following a sound source is also demonstrated. The sound source separation approach we propose is able to achieve a 13.7 dB improvement in signal-to-noise ratio compared to a single microphone when three speakers are present. In these conditions, the system demonstrates more than 80% accuracy on digit recognition, higher than most human listeners could obtain in our small case study when recognising only one of these sources. All these new capabilities will allow humans to interact more naturally with a mobile robot in real life settings.

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Robust Recognition of Simultaneous Speech By a Mobile Robot

This paper describes a system that gives a mobile robot the ability to perform automatic speech recognition with simultaneous speakers. A microphone array is used along with a real-time implementation of Geometric Source Separation and a post-filter that gives a further reduction of interference from other sources. The post-filter is also used to estimate the reliability of spectral features and compute a missing feature mask. The mask is used in a missing feature theory-based speech recognition system to recognize the speech from simultaneous Japanese speakers in the context of a humanoid robot. Recognition rates are presented for three simultaneous speakers located at 2 meters from the robot. The system was evaluated on a 200 word vocabulary at different azimuths between sources, ranging from 10 to 90 degrees. Compared to the use of the microphone array source separation alone, we demonstrate an average reduction in relative recognition error rate of 24% with the post-filter and of 42% when the missing features approach is combined with the post-filter. We demonstrate the effectiveness of our multi-source microphone array post-filter and the improvement it provides when used in conjunction with the missing features theory.

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A Full-Bandwidth Audio Codec With Low Complexity And Very Low Delay

We propose an audio codec that addresses the low-delay requirements of some applications such as network music performance. The codec is based on the modified discrete cosine transform (MDCT) with very short frames and uses gain-shape quantization to preserve the spectral envelope. The short frame sizes required for low delay typically hinder the performance of transform codecs. However, at 96 kbit/s and with only 4 ms algorithmic delay, the proposed codec out-performs the ULD codec operating at the same rate. The total complexity of the codec is small, at only 17 WMOPS for real-time operation at 48 kHz.

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An Iterative Linearised Solution to the Sinusoidal Parameter Estimation Problem

Signal processing applications use sinusoidal modelling for speech synthesis, speech coding, and audio coding. Estimation of the model parameters involves non-linear optimisation methods, which can be very costly for real-time applications. We propose a low-complexity iterative method that starts from initial frequency estimates and converges rapidly. We show that for N sinusoids in a frame of length L, the proposed method has a complexity of O(LN), which is significantly less than the matching pursuits method. Furthermore, the proposed method is shown to be more accurate than the matching pursuits and time-frequency reassignment methods in our experiments.

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