On the variation and invertibility of room impulse response functions

Mourjopoulos, John

doi:10.1016/s0022-460x(85)80054-7

Cited by 108 publications

(56 citation statements)

References 6 publications

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“…Our empirical results are in accordance with the observation reported in [40], where it is stated that inverse filtering increases the distortion when a response recorded at a different position is employed for dereverberation. Radlovic et al [45] gave a theoretical analysis on the sensitivity of inverse filtering.…”

Section: B Sensitivity Of Inverse Filteringsupporting

confidence: 92%

A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

Jin

Wang

2009

IEEE Trans. Audio Speech Lang. Process.

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Abstract-A major source of signal degradation in real environments is room reverberation. Monaural speech segregation in reverberant environments is a particularly challenging problem. Although inverse filtering has been proposed to partially restore the harmonicity of reverberant speech before segregation, this approach is sensitive to specific source/receiver and room configurations. This paper proposes a supervised learning approach to monaural segregation of reverberant voiced speech, which learns to map from a set of pitch-based auditory features to a grouping cue encoding the posterior probability of a time-frequency (T-F) unit being target dominant given observed features. We devise a novel objective function for the learning process, which directly relates to the goal of maximizing signal-to-noise ratio. The models trained using this objective function yield significantly better T-F unit labeling. A segmentation and grouping framework is utilized to form reliable segments under reverberant conditions and organize them into streams. Systematic evaluations show that our approach produces very promising results under various reverberant conditions and generalizes well to new utterances and new speakers.

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Section: B Sensitivity Of Inverse Filteringsupporting

confidence: 92%

A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

Jin

Wang

2009

IEEE Trans. Audio Speech Lang. Process.

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“…In the single-point case, the loudspeaker-room response is equalized for a 'sweet spot,' which may be the main listening position or a microphone location. However, this approach usually decreases the sound quality at other locations in the room, and thus is rarely a good solution [164,172,173]. In multi-point room equalization, the response is measured at two or more listening positions, and the equalizer is designed to improve the response at all of them.…”

Section: Room Equalizationmentioning

confidence: 99%

All About Audio Equalization: Solutions and Frontiers

Välimäki

Reiss

2016

Applied Sciences

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Audio equalization is a vast and active research area. The extent of research means that one often cannot identify the preferred technique for a particular problem. This review paper bridges those gaps, systemically providing a deep understanding of the problems and approaches in audio equalization, their relative merits and applications. Digital signal processing techniques for modifying the spectral balance in audio signals and applications of these techniques are reviewed, ranging from classic equalizers to emerging designs based on new advances in signal processing and machine learning. Emphasis is placed on putting the range of approaches within a common mathematical and conceptual framework. The application areas discussed herein are diverse, and include well-defined, solvable problems of filter design subject to constraints, as well as newly emerging challenges that touch on problems in semantics, perception and human computer interaction. Case studies are given in order to illustrate key concepts and how they are applied in practice. We also recommend preferred signal processing approaches for important audio equalization problems. Finally, we discuss current challenges and the uncharted frontiers in this field. The source code for methods discussed in this paper is made available at https://code.soundsoftware.ac.uk/projects/allaboutaudioeq.

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“…To simulate both convolutive and additive distortions in our mixture signals, we specify in each configuration a random location for the microphone and then choose two other locations for two sources (target and interference) randomly but control source-microphone distances to ensure that close-talking scenarios are avoided and signal-to-reverberant energy ratios are roughly constant in each simulated room. Note that, even in the same room, the RIRs from different source locations to the microphone may differ significantly [23], [27]. Consequently, a reverberant mixture is created by convolving each source with its corresponding RIR and mixing the two reverberant sources at 0-dB SNR.…”

Section: A Data Preparationmentioning

confidence: 99%

Reverberant Speech Segregation Based on Multipitch Tracking and Classification

Jin

Wang

2011

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Room reverberation creates a major challenge to speech segregation. We propose a computational auditory scene analysis approach to monaural segregation of reverberant voiced speech, which performs multipitch tracking of reverberant mixtures and supervised classification. Speech and nonspeech models are separately trained, and each learns to map from a set of pitch-based features to a grouping cue which encodes the posterior probability of a time-frequency (T-F) unit being dominated by the source with the given pitch estimate. Because interference may be either speech or nonspeech, a likelihood ratio test selects the correct model for labeling corresponding T-F units. Experimental results show that the proposed system performs robustly in different types of interference and various reverberant conditions, and has a significant advantage over existing systems.

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On the variation and invertibility of room impulse response functions

Cited by 108 publications

References 6 publications

A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

All About Audio Equalization: Solutions and Frontiers

Reverberant Speech Segregation Based on Multipitch Tracking and Classification

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