The Experts below are selected from a list of 44400 Experts worldwide ranked by ideXlab platform
Aurelio Uncini - One of the best experts on this subject based on the ideXlab platform.
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Audio Signal processing by neural networks
Neurocomputing, 2003Co-Authors: Aurelio UnciniAbstract:Abstract In this paper a review of architectures suitable for nonlinear real-time Audio Signal processing is presented. The computational and structural complexity of neural networks (NNs) represent in fact, the main drawbacks that can hinder many practical NNs multimedia applications. In particular efficient neural architectures and their learning algorithm for real-time on-line Audio processing are discussed. Moreover, applications in the fields of (1) Audio Signal recovery, (2) speech quality enhancement, (3) nonlinear transducer linearization, (4) learning based pseudo-physical sound synthesis, are briefly presented and discussed.
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Audio Signal processing by neural networks
Neurocomputing, 2003Co-Authors: Aurelio UnciniAbstract:In this paper a review of architectures suitable for nonlinear real-time Audio Signal processing is presented. The computational and structural complexity of neural networks (NNs) represent in fact, the main drawbacks that can hinder many practical NNs multimedia applications. In particular efficient neural architectures and their learning algorithm for real-time on-line Audio processing are discussed. Moreover, applications in the fields of (1) Audio Signal recovery, (2) speech quality enhancement, (3) nonlinear transducer linearization, (4) learning based pseudo-physical sound synthesis, are briefly presented and discussed. © 2003 Elsevier B.V. All rights reserved.
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Audio Signal processing byneural networks
2003Co-Authors: Aurelio UnciniAbstract:In this paper a review of architectures suitable for nonlinear real-time Audio Signal processing is presented. The computational and structural complexityof neural networks (NNs) represent in fact, the main drawbacks that can hinder manypractical NNs multimedia applications. In particular e,cient neural architectures and their learning algorithm for real-time on-line Audio processing are discussed. Moreover, applications in the -elds of (1) Audio Signal recovery, (2) speech qualityenhancement, (3) nonlinear transducer linearization, (4) learning based pseudo-phy sical sound synthesis, are brie1y presented and discussed. c
Geoffroy Peeters - One of the best experts on this subject based on the ideXlab platform.
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OBJECTIVE CHARACTERIZATION OF Audio Signal QUALITY: APPLICATIONS TO MUSIC COLLECTION DESCRIPTION
2017Co-Authors: Dominique Fourer, Geoffroy PeetersAbstract:In this paper, we propose a set of Audio features to describe the quality of an Audio Signal. Audio quality is here considered as being modified by the chain of processes/effects applied to the individual instrument tracks to obtain the final mix of a musical piece. Thus, the quality also depends on the mastering processes applied to the final mix or the Signal degradation caused by MP3 compression. To evaluate our proposal, we created a large set of artificial mixes and also used real-world studio mixes. Using unsupervised and supervised classification methods, we show that our proposed Audio features can detect the processing chain. Since this processing chain applied in professional studio has evolved over the years, we use our Audio features to directly predict the decade during which a music track was recorded. Index Terms— Audio quality, music information retrieval , Audio reverse-engineering, database indexing, music remixing.
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Spectral and temporal periodicity representations of rhythm for the automatic classification of music Audio Signal
IEEE Transactions on Audio Speech and Language Processing, 2011Co-Authors: Geoffroy PeetersAbstract:Spectral and temporal periodicity representations of rhythm for the automatic classification of music Audio Signal
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Chroma-based estimation of musical key from Audio-Signal analysis
2006Co-Authors: Geoffroy PeetersAbstract:Chroma-based estimation of musical key from Audio-Signal analysis
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Musical key estimation of Audio Signal based on HMM modeling of chroma vectors
2006Co-Authors: Geoffroy PeetersAbstract:Musical key estimation of Audio Signal based on HMM modeling of chroma vectors
Biing-hwang Juang - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Audio Signal classification with temporal envelopes
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: M. Umair Bin Altaf, Biing-hwang JuangAbstract:The conventional approach to Audio processing, based on the short-time power spectrum model, is not adequate when it comes to general Audio Signals. We propose an approach, justified by studies from psycho-acoustics and neuroimaging, which uses the magnitude and frequency envelope of the Audio Signal in the from of AM-FM modulations to build an ARMA model which is then fed to a GMM to classify into various Audio classes. We show that it makes explicit certain aspects of the Signal which are overlooked when processing is limited to the spectral domain.
Nikos Nikolaidis - One of the best experts on this subject based on the ideXlab platform.
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robust Audio watermarking in the time domain
IEEE Transactions on Multimedia, 2001Co-Authors: Paraskevi Bassia, Ioannis Pitas, Nikos NikolaidisAbstract:The Audio watermarking method proposed in this paper offers copyright protection to an Audio Signal by time domain processing. The strength of Audio Signal modifications is limited by the necessity to produce an output Signal that is perceptually similar to the original one. The watermarking method presented here does not require the use of the original Signal for watermark detection. The watermark Signal is generated using a key, i.e., a single number known only to the copyright owner. Watermark embedding depends on the Audio Signal amplitude and frequency in a way that minimizes the audibility of the watermark Signal. The embedded watermark is robust to common Audio Signal manipulations like MPEG Audio coding, cropping, time shifting, filtering, resampling, and requantization.
Tong Xue - One of the best experts on this subject based on the ideXlab platform.
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Watermark Embedding Algorithm for Digital Audio Signal
Computer Engineering, 2003Co-Authors: Tong XueAbstract:In this paper,a new watermarking algorithm based on wavelet transform for digital Audio Signal is proposed. This watermark scheme uses the user input data to produce a robust watermark in the wavelet domain. The original Audio Signal can be fully recovery from the watermarked Signal by this algorithm. The performance of the proposed watermark algorithm is supported by experimental results. Initial results obtained show that the watermarked Signal is indistinguishable from the original Signal and the watermark message is successfully extracted even after the watermarked Audio Signal were attacked.
