667 resources related to Audio Compression
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ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.
Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies
ICSP2018 includes sessions on all aspects of theory, design and applications of signal processing. Prospective authors are invited to propose papers in any of the following areas, but not limited to: A. Digital Signal Processing (DSP)B. Spectrum Estimation & ModelingC. TF Spectrum Analysis & WaveletD. Higher Order Spectral AnalysisE. Adaptive Filtering &SPF. Array Signal ProcessingG. Hardware Implementation for Signal ProcessingH Speech and Audio CodingI. Speech Synthesis & RecognitionJ. Image Processing & UnderstandingK. PDE for Image ProcessingL.Video compression &StreamingM. Computer Vision & VRN. Multimedia & Human-computer InteractionO. Statistic Learning & Pattern RecognitionP. AI & Neural NetworksQ. Communication Signal processingR. SP for Internet and Wireless CommunicationsS. Biometrics & AuthentificationT. SP for Bio-medical & Cognitive ScienceU
With technically co-sponsored by IEEE ComSoc(Communications Society), IEEE ComSoc CISTC(Communications & Information Security Technical Community), and IEEE ComSoc ONTC(Optical Networking Technical Community), the ICACT(International Conference on Advanced Communications Technology) Conference has been providing an open forum for scholars, researchers, and engineers to the extensive exchange of information on newly emerging technologies, standards, services, and applications in the area of the advanced communications technology. The conference official language is English. All the presented papers have been published in the Conference Proceedings, and posted on the ICACT Website and IEEE Xplore Digital Library since 2004. The honorable ICACT Out-Standing Paper Award list has been posted on the IEEE Xplore Digital Library also, and all the Out-Standing papers are subjected to the invited paper of the "ICACT Transactions on the Advanced Communications Technology" Journal issued by GIRI
The general scope of the conference ranges from signal and image processing to telecommunication, and applications of signal processing methods in biomedical and communication problems.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The IEEE Standard for Advanced Audio Coding (IEEE 1857.2) is a new standard approved by IEEE in August 2013. The standard comprises both lossy and lossless audio compression tools. This paper presents the lossless audio compression tool, which utilizes a pre-processing procedure for flattening the amplitude envelop of linear prediction residue, and an arithmetic coder that adopts a scaled probability ...
2018 9th International Symposium on Telecommunications (IST), 2018
Graph-based Transform is one of the recent transform coding methods which has been used successfully in the state-of-art data decorrelation applications. In this paper, we propose a Graph-based Transform (GT) for audio compression. Hence, we introduce a proper graph structure for audio. Then the audio frames are projected onto an orthogonal matrix consisting of eigenvectors of the introduced graph matrix, ...
2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017
This paper considers implementation of audio compression using the lossless compression techniques like dynamic Huffman coding and Run Length Encoding (RLE). Audio file is firstly preprocessed to find sampling frequency and the encoded data bits in sample audio file. After that dynamic Huffman and RLE is applied. The design of dynamic Huffman coding technique involves evaluation of the probabilities of ...
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
This paper introduces a novel prediction structure for improving the lossless compression ratio, by accounting for companding nonlinearities of different sample-based audio formats. This applies to a wide class of formats including a-law, μ-law, DAT-LP (digital audio tape recorders), DV-LP (digital video camcorders), and HDCD (high definition compatible digital). The proposed prediction structure obtains significant compression improvements (8-12%) over traditional ...
Proceedings of Data Compression Conference - DCC '96, 1996
Summary form only given. Multimedia computing is becoming of increasing importance to modern telecommunications. Audio compression is an integral part of multimedia applications. The unique operating environment of multimedia computing imposes many unique requirements on the audio compression algorithm, including a high compression rate, low-complexity, and the ability of handling different audio sources. On the other hand, these requirements are ...
Interview with Bart Kosko, 2012: CIS Oral History Project
ICASSP 2011 Trends in Multimedia Signal Processing
Capturing Sound with Smoke and Lasers
Standards Education: An Introduction (Chinese subtitles)
ICASSP 2011 Trends in Machine Learning for Signal Processing
Bose Corporation - IEEE Corporate Innovation Award, 2019 IEEE Honors Ceremony
Computing Conversations: Daphne Koller and Coursera
Open Sesame: Design Guidelines for Invisible Passwords
ICASSP 2011 Trends in Design and Implementation of Signal Processing Systems
2011 IEEE Richard W. Hamming Medal - Toby Berger
2011 IEEE Awards James H. Mulligan, Jr. Education Medal - Raj Mittra
Standards Education: Strategic Standardization (with Chinese subtitles)
Noise-Shaped Active SAR Analog-to-Digital Converter - IEEE Circuits and Systems Society (CAS) Distinguished Lecture
Standards Education: Creating Global Standards (Chinese subtitles)
IEEE Magnetics Distinguished Lecture - Alison B. Flatau
Kees Schouhamer Immink accepts the IEEE Medal of Honor - Honors Ceremony 2017
ICASSP 2010 - New Signal Processing Application Areas
Massive MIMO Active Antenna Arrays for Advanced Wireless Communications: IEEE CAS lecture by Dr. Mihai Banu
The IEEE Standard for Advanced Audio Coding (IEEE 1857.2) is a new standard approved by IEEE in August 2013. The standard comprises both lossy and lossless audio compression tools. This paper presents the lossless audio compression tool, which utilizes a pre-processing procedure for flattening the amplitude envelop of linear prediction residue, and an arithmetic coder that adopts a scaled probability template. The performance of the new IEEE lossless compressor is evaluated and compared with state-of-the-art lossless audio coders. Evaluation results show that the lossless compression performance of the IEEE compressor is about 5% higher than MPEG-4 ALS and 12% higher than FLAC.
Graph-based Transform is one of the recent transform coding methods which has been used successfully in the state-of-art data decorrelation applications. In this paper, we propose a Graph-based Transform (GT) for audio compression. Hence, we introduce a proper graph structure for audio. Then the audio frames are projected onto an orthogonal matrix consisting of eigenvectors of the introduced graph matrix, leading to the sparse coefficients. The results show that the proposed method outperforms the conventional transform methods like Discrete Cosine Transform (DCT) and Walsh-Hadamard Transform (WHT) in decorrelation of the audio signals.
This paper considers implementation of audio compression using the lossless compression techniques like dynamic Huffman coding and Run Length Encoding (RLE). Audio file is firstly preprocessed to find sampling frequency and the encoded data bits in sample audio file. After that dynamic Huffman and RLE is applied. The design of dynamic Huffman coding technique involves evaluation of the probabilities of occurrence “on the fly”, as the ensemble is being transmitted and RLE is based on finding the runs of the data i.e. repeating strings and replacing it by single data element and its count. These techniques work with a common goal to obtain the utmost possible compression ratio and less Time Elapsed to compress. The competence of the proposed methods is verified by applying these techniques to variety of audio data. Stimulus behind this work is to offer a detail analysis of lossless compression methods and finding the one which is best suited for compression of multimedia data in cognitive radio environment.
This paper introduces a novel prediction structure for improving the lossless compression ratio, by accounting for companding nonlinearities of different sample-based audio formats. This applies to a wide class of formats including a-law, μ-law, DAT-LP (digital audio tape recorders), DV-LP (digital video camcorders), and HDCD (high definition compatible digital). The proposed prediction structure obtains significant compression improvements (8-12%) over traditional linear prediction for a-law, μ-law, DAT-LP, DV-LP and also small compression improvements for HDCD. The improvement in compression can also be used for the detection of nonlinearities in HDCD format, making possible to play HDCD CDs at improved audio resolution in ordinary public domain players.
Summary form only given. Multimedia computing is becoming of increasing importance to modern telecommunications. Audio compression is an integral part of multimedia applications. The unique operating environment of multimedia computing imposes many unique requirements on the audio compression algorithm, including a high compression rate, low-complexity, and the ability of handling different audio sources. On the other hand, these requirements are not met by most of the audio compression methods that have been developed. We present a low-complexity wavelet based audio coding algorithm that is capable of handling arbitrary audio sources, including music and speech. The algorithm transforms incoming audio data into the wavelet domain, and compresses data by exploring the redundancy in the wavelet coefficients. A bit rate control scheme is designed such that the algorithm operates at virtually any preselected bit rate. This algorithm is of very low complexity, and can be implemented in real-time using a the 486DX-33 or faster personal computer. The superior performance of this algorithm is also demonstrated by comparing it with several other popular audio compression techniques. This algorithm meets or exceeds the requirements of multimedia computing and could be used as a standard for such applications.
Speech compression is the technology of converting human speech into an efficient encoded representation that can be decoded to produce a close approximation of the original signal. In this paper, we propose a new algorithm which compresses speech signals using a wavelet compression technique. The performance of this method is compared against the following representative coding and compression schemes: adaptive differential pulse code modulation (ADPCM) which reduces the transmitted data by a factor of two; linear predictive coding (LPC) with compression ratio of more than twelve to one; linear predictive coding algorithm using the United States Department of Defense Standard 1015 with compression ratio of 26:1; Global System Mobile (GSM) algorithm which reduces the transmitted data by a factor of five. The following parameters are compared: (i) quality of the reconstructed signal after decoding; (ii) compression ratios. (iii) signal to noise ratio (SNR); (iv) peak signal to noise ratio (PSNR); (v) normalized root mean square error (NRMSE).
With increasing factory automation application requiring multimedia content, compression technology for audiovisual content is needed to minimal bandwidth requirement. An audio compression scheme is presented in here and this scheme is characterized primarily by its fast encoding and decoding speed for high fidelity audio. The bit rate for this scheme is competitive to existing international standard for high fidelity audio compression. In the context of factory automation, the compression scheme presented in here has desirable attribute including preservation of high quantitative fidelity, fast encoding speed using off-the-shelf microcontroller with integer only computation for low cost applications, minimal codec delay to satisfy real time requirement, etc.
The paper proposes a technique for scalable to lossless audio compression. The scheme presented is perceptually scalable and also provides for lossless compression. It produces smooth objective scalability, in terms of SegSNR, from lossy to lossless compression. The proposal is built around the introduced perceptual SPIHT algorithm, which is a modification of the SPIHT algorithm. Both objective and subjective results are reported and demonstrate both perceptual and objective measure scalability. The subjective results indicate that the proposed method performs comparably with the MPEG-4 AAC coder at 16, 32 and 64 kbps, yet also achieves a scalable-to-lossless architecture.
This paper provides a subjective quality analysis of transforms used in audio compression algorithms for a class of music signals. An analysis with the discrete wavelet packet transform (DWPT) compares performances of an eight- level non-uniform critical-band frequency split versus a five-level uniform subband frequency split used in MPEG. Another analysis compares the performances of the DWPT versus the modified discrete cosine transform (MDCT). The quality assessor at each level of compression was based on a listening test (34 subjects). Results show that the critical-band split provides significantly better quality than the even-band split for sounds with strong low frequency content, and that the DWPT outperforms the MDCT overall, but with significant improvement for non-tonal sounds.
Summary form only given. We propose a new paradigm for lossless audio compression, where prediction and residual coding are combined in a single logical stage, taking advantage of the fine statistical structure of the original signal. We obtained significant compression gains for integer signals which were normalized, companded, or recorded using DAT-LP. We compared our existing lossless audio compressor OptimFROG and the new modified variant on a large corpus of 73 Audio CDs. The encoding time was increased with 9.6% and the decoding time with 6.1%. Overall, OptimFROG (normal) obtained a compression (compressed size/original size) of 55.96%, the new variant 55.36%, and Monkey's Audio (high) 56.10%. For 19 CDs we obtained compression improvements up to 12.35% and on average 2.14%, for 14 CDs we obtained small compression improvements on average 0.24%, and the remaining CDs did not present improvements. For mu-law decoded, A-law decoded, and DAT-LP audio, we obtained on one file compression improvements of 28.66%, 22.56%, and 20.98%, respectively. The new paradigm can be also applied to 24 bit lossless audio compression and to lossless image compression (further favored by the small bit depth per color)
No standards are currently tagged "Audio Compression"