scholarly journals Codificação perceptiva de áudio por meio de decomposições atômicas em exponenciais complexas

2019 ◽  
Vol 1 (46) ◽  
pp. 196
Author(s):  
Valmir Dos Santos Nogueira Junior ◽  
Michel Pompeu Tcheou ◽  
Flávio Rainho Ávila
Keyword(s):  
Matching Pursuit ◽  
Rate Distortion ◽  
Compact Representation ◽  
Audio Signals ◽  
Audio Compression ◽  
Audio Quality ◽  
Perceptual Evaluation ◽  
Layer I ◽  
Analysis System ◽  
Complex Exponential

<p class="Standard">The atomic decomposition of signals by algorithm of the class “Matching Pursuit” (MP) has been applied in audio compression. Literature review suggests that, the use of psychoacoustic criteria allows a more compact representation of the signal, without loss of perceived quality. This work presents the implementation of an analysis system by synthesis of audio signals using MP associated with the use of psychoacoustic global masking threshold, inspired by MPEG layer I, as well as Complex Exponential Dictionaries (DEC). For the compression of the signal, we used the optimization of rate-distortion by operational curves, adjusting the Lagrange multiplier. The performance of the compression method for different types of signals is evaluated by an objective measurement standardized by the International Telecommunications Union (ITU), the PEAQ (Perceptual Evaluation of Audio Quality) based on the bit rate per sample, obtaining satisfactory results.</p>

A Backward Compatible Multichannel Audio Compression Method

2013 ◽  
Vol 756-759 ◽  
pp. 977-981
Author(s):  
Xue Fei Gao ◽  
Guo Yang ◽  
Jing Wang ◽  
Xiang Xie ◽  
Jing Ming Kuang
Keyword(s):  
Audio Signal ◽  
Temporal Analysis ◽  
Audio Signals ◽  
Audio Compression ◽  
Compression Method ◽  
Audio Quality ◽  
Multichannel Audio ◽  
Parametric Data ◽  
Encoding Method ◽  
Spatial Temporal Analysis

This paper proposes a backward-compatible multichannel audio codec based on downmix and upmix operation. The codec represents a multichannel audio input signal with downmixed mono signal and spatial parametric data. The encoding method consists of three parts: spatial temporal analysis of audio signal, compressing multi-channel audio into mono audio and encoding mono signals. The proposed codec combines high audio quality and low parameter coding rate and the method is simpler and more effective than the conventional methods. With this method, its possible to transmit or store multi-channel audio signals as mono audio signals.

Data Hiding for Stereo Audio Signals

2013 ◽  
pp. 104-128
Author(s):  
Kazuhiro Kondo
Keyword(s):  
Data Hiding ◽  
Audio Signal ◽  
Audio Coding ◽  
Original Signal ◽  
Audio Signals ◽  
Audio Quality ◽  
Input Source ◽  
Rate Conversion ◽  
Fixed Delay

This chapter proposes two data-hiding algorithms for stereo audio signals. The first algorithm embeds data into a stereo audio signal by adding data-dependent mutual delays to the host stereo audio signal. The second algorithm adds fixed delay echoes with polarities that are data dependent and amplitudes that are adjusted such that the interchannel correlation matches the original signal. The robustness and the quality of the data-embedded audio will be given and compared for both algorithms. Both algorithms were shown to be fairly robust against common distortions, such as added noise, audio coding, and sample rate conversion. The embedded audio quality was shown to be “fair” to “good” for the first algorithm and “good” to “excellent” for the second algorithm, depending on the input source.

Investigation of Lossless Audio Compression using IEEE 1857.2 Advanced Audio Coding

2017 ◽  
Vol 6 (2) ◽  
pp. 422
Author(s):  
Teddy Surya Gunawan ◽  
Muhammad Khalif Mat Zain ◽  
Fathiah Abdul Muin ◽  
Mira Kartiwi
Keyword(s):  
Lossless Compression ◽  
Audio Coding ◽  
Audio Compression ◽  
Source File ◽  
Compression Performance ◽  
Audio Quality ◽  
Current State ◽  
Ieee Standard ◽  
Audio Files ◽  
Advanced Audio Coding

<p>Audio compression is a method of reducing the space demand and aid transmission of the source file which then can be categorized by lossy and lossless compression. Lossless audio compression was considered to be a luxury previously due to the limited storage space. However, as storage technology progresses, lossless audio files can be seen as the only plausible choice for those seeking the ultimate audio quality experience. There are a lot of commonly used lossless codecs are FLAC, Wavpack, ALAC, Monkey Audio, True Audio, etc. The IEEE Standard for Advanced Audio Coding (IEEE 1857.2) is a new standard approved by IEEE in 2013 that covers both lossy and lossless audio compression tools. A lot of research has been done on this standard, but this paper will focus more on whether the IEEE 1857.2 lossless audio codec to be a viable alternative to other existing codecs in its current state. Therefore, the objective of this paper is to investigate the codec’s operation as initial measurements performed by researchers show that the lossless compression performance of the IEEE compressor is better than any traditional encoders, while the encoding speed is slower which can be further optimized.</p>

Comparative Performance Evaluation of Greedy Algorithms for Speech Enhancement System

2020 ◽  
pp. 2150017
Author(s):  
Bittu Kumar
Keyword(s):  
Speech Enhancement ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Speech Quality ◽  
Orthogonal Matching Pursuit ◽  
Objective Measures ◽  
Comparative Performance ◽  
Perceptual Evaluation ◽  
Generalized Orthogonal ◽  
Recovery Algorithms

In this paper, the performance of compressive sensing (CS)-based technique for speech enhancement has been studied and results analyzed with recovery algorithms as a comparison of their performances. This is done for several recovery algorithms such as matching pursuit, orthogonal matching pursuit, stage-wise orthogonal matching pursuit, compressive sampling matching pursuit and generalized orthogonal matching pursuit. Performances of all these greedy algorithms were compared for speech enhancement. The evaluation of results has been carried out using objective measures (perceptual evaluation of speech quality, log-likelihood ratio, weighted spectral slope distance and segmental signal-to-noise ratio), simulation time and composite objective measures (signal distortion C[Formula: see text], background intrusiveness C[Formula: see text] and overall quality C[Formula: see text]. Results showed that the CS-based technique using generalized orthogonal matching pursuit algorithm yields better performance than the other recovery algorithms in terms of speech quality and distortion.

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