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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Brzuchalski, Grzegorz Warsaw Univ Technol Inst Radioelect Warsaw Poland

ISBN: (纸本)9781628413694

This paper addresses the optimisation problem of Huffman encoding in MPEG-4 advanced audio coding standard. At first, the Huffman encoding problem and the need of encoding two side info parameters scale factor and Huffman codebook are presented. Next, Two Loop Search, Maximum Noise Mask Ratio and Trellis Based algorithms of bit allocation are briefly described. Further, Huffman encoding optimisation are shown. New methods try to check and change scale factor bands as little as possible to estimate bitrate cost or its change. Finally, the complexity of old and new methods is calculated, compared and measured time of encoding is given.

关键词： AAC advanced audio coding Huffman optimisation Huffman Code Books Scale Factors Two Loop Search Trellis Search BFOS

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Huffman coding in advanced audio coding standard

Huffman coding in Advanced Audio Coding standard

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Brzuchalski, Grzegorz Warsaw Univ Technol Inst Radioelect PL-00665 Warsaw Poland

ISBN: (纸本)9780819491718

This article presents hardware architectures of advanced audio coding (AAC) Huffman noiseless encoder, its optimisations and working implementation. Much attention has been paid to optimise the demand of hardware resources especially memory size. The aim of design was to get as short binary stream as possible in this standard. The Huffman encoder with whole audio-video system has been implemented in FPGA devices.

关键词： MPEG-4 AAC advanced audio coding Quantisation Huffman Lossless encoding FPGA

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Quantization and psychoacoustic model in audio coding in advanced audio coding

Quantization and psychoacoustic model in audio coding in Adv...

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Brzuchalski, Grzegorz Warsaw Univ Technol Inst Radioelect Div Televis Digital Media Grp PL-00665 Warsaw Poland

ISBN: (纸本)9780819485823

This paper presents complete optimized architecture of advanced audio Coder quantization with Huffman coding. After that psychoacoustic model theory is presented and few algorithms described: standard Two Loop Search, its modifications, Genetic, Just Noticeable Level Difference, Trellis-Based and its modification: Cascaded Trellis-Based Algorithm.

关键词： AAC advanced audio coding quantization huffman coding psychoacoustic model fpga

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Energy Balance in advanced audio coding Encoder bit-distortion loop algorithm

Energy Balance in Advanced Audio Coding Encoder bit-distorti...

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Brzuchalski, Grzegorz Pastuszak, Grzegorz Warsaw Univ Technol Inst Radioelect PL-00665 Warsaw Poland

ISBN: (纸本)9780819497857

The paper presents two techniques of balancing energy in ScaleFactor bands for advanced audio coding. The techniques allows the AAC encoder to get a better audio quality. The first one modifies Scale Factors assigned to each band after the quantization whereas the second finds and changes offsets in the quantization - just before rounding down. The implementations of the algorithms have been tested and results discussed. Results show that these techniques significantly improve the quality. At last hardware implementation possibilities are discussed.

关键词： MPEG-4 AAC advanced audio coding Quantisation Huffman Lossyen coding FPGA

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Deep learning of chroma representation for cover song identification in compression domain

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2018年第3期29卷 887-902页

作者： Fang, Jiunn-Tsair Chang, Yu-Ruey Chang, Pao-Chi Ming Chuan Univ Dept Elect Engn 5 Deming Rd Taoyuan 33348 Taiwan Natl Cent Univ Dept Commun Engn 300 Jhongda Rd Taoyuan 32001 Taiwan

Methods for identifying a cover song typically involve comparing the similarity of chroma features between the query song and another song in the data set. However, considerable time is required for pairwise comparisons. In addition, to save disk space, most songs stored in the data set are in a compressed format. Therefore, to eliminate some decoding procedures, this study extracted music information directly from the modified discrete cosine transform coefficients of advanced audio coding and then mapped these coefficients to 12-dimensional chroma features. The chroma features were segmented to preserve the melodies. Each chroma feature segment was trained and learned by a sparse autoencoder, a deep learning architecture of artificial neural networks. The deep learning procedure was to transform chroma features into an intermediate representation for dimension reduction. Experimental results from a covers80 data set showed that the mean reciprocal rank increased to 0.5 and the matching time was reduced by over 94% compared with traditional approaches.

关键词： Cover song Music retrieval Sparse autoencoder Descriptor advanced audio coding

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Detection of AAC compression using MDCT-based features and supervised learning

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2022年第3期34卷 451-468页

作者： Garcia-Hernandez, Jose Juan Gomez-Flores, Wilfrido IPN Unidad Tamaulipas Ctr Invest & Estudios Avanzados Ciudad Victoria TA Mexico

audio files are frequent targets of malicious users who seek illegal profit trading with fake-quality content. For increasing the confidence in the integrity of audio files, the detection of fake-quality content is an important task. This paper proposes a method for detecting advanced audio coding (AAC) compression on suspicious WAV files, in which the variance of the Modified Discrete Cosine Transform (MDCT) characterises four compression bitrates: uncompressed, 64 kbps, 128 kbps, and 256 kbps. This scheme takes advantage of the reduction of the variance of the high-frequency MDCT coefficients in compressed signals. Data obtained from MDCT coefficients generate a high-dimensional feature space. Hence, Principal Component Analysis, followed by Linear Discriminant Analysis, is used for projecting the high-dimensional data onto a lower-dimensional space. Besides, six supervised learning algorithms are compared for classifying four compression bitrates. The experiments show that using audio samples with 20 seconds and 1024 MDCT coefficients, an accuracy of 93% is reached with a Bayesian classifier. Collaterally, the detection between uncompressed and compressed signals attains an accuracy of 97% with Multinomial Logistic Regression. In conclusion, the proposed approach can detect previous AAC compression and can be potentially used when it is unfeasible to recover the suspicious signal completely.

关键词： advanced audio coding modified discrete cosine transform bitrate classification dimensionality reduction supervised learning

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