Lossless Audio Research Articles

Lossless audio CODEC using non-repeated dynamic block encoding

Lossless audio CODEC using non-repeated dynamic block encoding

Lossless audio codec based on CNN, weighted tree and arithmetic encoding (LACCWA)

Lossless audio codec based on CNN, weighted tree and arithmetic encoding (LACCWA)

A Lossless Audio Encryption Method based on Chebyshev Map

This paper presents a lossless and secure audio encryption method based on the chaotic Chebyshev map. Firstly, the input audio samples are preprocessed to obtain the integer and decimal parts. The integer parts are rescaled to the interval [0,255]. By iterating the Chebyshev map in the chaotic range using plaintext-dependent variables, the integer parts of the input audio sample are scrambled and then diffused. Finally, a post-processing operation is applied to the diffused audio samples. Keyspace and key sensitivity analysis, histogram analysis, the correlation between adjacent samples analysis, information entropy analysis, number of sample change rate analysis, and speed analysis results are presented. These security analysis results show that the proposed audio encryption method can be used in secure voice transmission applications.

Standard Evolution of 5G-Advanced and Future Mobile Network for Extended Reality and Metaverse

eXtend Reality (XR) and Metaverse are important service scenarios to be supported by 5G-Advanced (5G-A) and future mobile networks. However, there are still many challenges and gaps for 5G-A to guarantee the immersive experience of XR and Metaverse. This article analyzes the challenges to support XR in 5G-A, e.g., lossless audio compression transmission, full-view high-resolution video transmission, and synchronization between multi-modal transmission. Then an overall system architecture of 5G-A for enabling XR is proposed with the concept of application-network coordination. And the latest advances of 3GPP standard on network architecture enhancements for XR are overviewed, including network-assistant service optimization, Quality of Service (QoS) enhancements, and power saving optimization. Finally, the future standard and research directions for Metaverse are analyzed, including integrated architecture with communication-sensing-computing, immersive experience enhancements for new media types, intelligent network, security and privacy aspects.

Improving sign-algorithm convergence rate using natural gradient for lossless audio compression

In lossless audio compression, the predictive residuals must remain sparse when entropy coding is applied. The sign algorithm (SA) is a conventional method for minimizing the magnitudes of residuals; however, this approach yields poor convergence performance compared with the least mean square algorithm. To overcome this convergence performance degradation, we propose novel adaptive algorithms based on a natural gradient: the natural-gradient sign algorithm (NGSA) and normalized NGSA. We also propose an efficient natural-gradient update method based on the AR(p) model, which requires mathcal {O}(p) multiply–add operations at every adaptation step. In experiments conducted using toy and real music data, the proposed algorithms achieve superior convergence performance to the SA. Furthermore, we propose a novel lossless audio codec based on the NGSA, called the natural-gradient autoregressive unlossy audio compressor (NARU), which is open-source and implemented in C. In a comparative experiment with existing, well-known codecs, NARU exhibits superior compression performance. These results suggest that the proposed methods are appropriate for practical applications.

Designing a novel lossless audio compression technique with the help of optimized graph traversal (LACOGT)

Designing a novel lossless audio compression technique with the help of optimized graph traversal (LACOGT)

Advanced forensic procedure for the authentication of audio recordings generated by Voice Memos application of iOS14.

In this study, we propose an advanced forensic examination procedure for audio recordings generated by the Voice Memos application with iPhone Operation System (iOS)14, to verify that these are the original recordings and have not been manipulated. The proposed examination procedure consists of an analysis of the characteristics of audio recordings and of the file system of the device storing the audio recordings. To analyze the characteristics of audio recordings, we compare the encoding parameters (bitrate, sampling rate, timestamps, etc.) and the file structure to determine whether audio recordings were manipulated. Next, in the device examination step, we analyze the media-log history and temporary files of the file system obtained by mobile forensic tools. For comparative analysis, a total of 100 audio recording samples were obtained through the Voice Memos application from five iPhone mobile handsets of different models with iOS14 installed using Advanced audio coding (AAC) or Apple lossless audio codec (ALAC). As a result of analyzing the encoding parameters between the original and manipulated audio recordings, as well as the temporary files contained in the device file system, the difference in the encoding parameters and the very unique trace of the original audio recordings in the temporary files were confirmed when manipulating the audio recordings. In particular, the primary advantage of our proposed method is its potential ability to recover original audio recordings that were subsequently manipulated via the temporary files examined in the device file system analysis.

A method of forensic authentication of audio recordings generated using the Voice Memos application in the iPhone

Considering the widespread use of mobile phones, audio recordings of crime scenes are widely used as digital evidence; however, it is important to authenticate the audio recordings before consideration as legal evidence. This study aimed to develop a method to authenticate audio recordings generated using the iPhone through three steps: 1) bitrate/audio latency time analysis of audio recordings, 2) comparison of the file structure/timestamp on audio recordings, and 3) device-based log history examinations for the provenance of audio recordings. Herein, we analyzed audio recording samples from ten different models of mobile handsets of the iPhone with Advanced Audio Coding (AAC) or Apple Lossless Audio Codec (ALAC), through the Voice Memos application depending on the iPhone Operating System (iOS). To analyze the characteristics of these audio recordings, we compared features including audio latency, file format/structure, and timestamps between the audio recordings generated in the iPhone and those edited through the built-in audio editing function. Furthermore, we investigated the log history registered in devices during the generation of the audio recordings. Differences in the audio latency, file size, timestamps, bitrate, and log history were confirmed on the iPhone when manipulating the audio recordings. The present results show that it is possible to verify the authentication of audio recordings generated using the Voice Memos application on iPhone.

Developing a Dynamic Cluster Quantization based Lossless Audio Compression (DCQLAC)

In this paper, an approach has been made to produce a compressed audio without losing any information. The proposed scheme is fabricated with the help of dynamic cluster quantization followed by Burrows Wheeler Transform (BWT) and Huffman coding. The encoding algorithm has been designed in two phases, i.e., dynamic cluster selection (of sampled audio) followed by dynamic bit selection for determining quantization level of individual cluster. Quantization level of each cluster is selected dynamically based on mean square quantization error (MSQE). Bit stream is further compressed by applying Burrows Wheeler Transform (BWT) and Huffman code respectively. Experimental results are supported with current state-of-the-art in audio quality analysis (like statistical parameters (compression ratio, space savings, SNR, PSNR) along with other parameters (encoding time, decoding time, Mean Opinion Score (MOS) and entropy) and compared with other existing techniques.

A robust watermarking method for MPEG-4 SLS audio

Audio lossless compression technology has been widely used to provide customers with higher quality music works. The protection of these works becomes very important. However, there are few digital watermarking methods specifically for lossless compressed audio, and the features of the lossless codec have not been used to improve the performance of watermarking. In this research, we propose a robust audio watermarking method to protect the music encoded by MPEG-4 SLS (Scalable Lossless Coding) format and improve the watermark security and robustness in two aspects. We design an adaptive selection method for watermark embedding position to enhance the security. The significant states combinations of consecutive scale factor-bands (SFB) are constructed as the feature data to determine the embedding target SFB. In this way, the embedding SFB adaptively changes with the audio signal variation, so the security improves. Based on the feature of the MPEG-4 SLS codec, we design new watermark embedding and extraction methods to improve the robustness. The watermark data are embedded into the target SFB. Meanwhile, the embedding is achieved through changing the probability distributions of Bit-plane coding results, which is stable when the audio faces attacks. The watermark data are extracted by comparing the probability distributions before and after embedding. The probability distributions are stable under attacks, so the robustness improves. The experimental data show that the proposed method can resist conventional signal processing attacks while guaranteeing the imperceptibility.

Encrypting an audio file based on integer wavelet transform and hand geometry

A new algorithm suggested for audio file encryption and decryption utilizing integer wavelet transform to take advantage of the property for adaptive context-based lossless audio coding. In addition, biometrics are used to give a significant level of classification and unwavering quality because the procedure has numerous qualities and points of interest. The offered algorithm utilized many properties of hand geometry estimations as keys to encode and decode the audio file. Many tests were carried out on a set of audio files and quality metrics such as mean square error and correlations were calculated which in turn confirmed the efficiency and quality of the work.

A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR method

In this paper, the improvement of the cascaded prediction method was presented. Three types of main predictor block with different levels of complexity were compared, including two complex prediction methods with backward adaptation, i.e., extension Active Level Classification Model (ALCM+) and extended Ordinary Least Square (OLS+). Our own approach to implementation of the effective context-dependent constant component removal block is also presented. Additionally, the improved adaptive arithmetic coder with short, medium and long-term adaptation was presented, and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

Performance Evaluation of Audio Coding by Amalgam AAC and FLAC Audio codec using MDCT and INTMDCT Algorithm

The MDCT and IntMDCT Algorithm is widely utilized is Audio coding.By lifting scheme or rounding operation IntegerMDCT is evolved from Modified Discrete Cosine Transform.This method acquire the properties of MDCT and contribute excelling invertiblity and good spectral mean.In this paper we discuss about the audio codec like AAC and FLAC using MDCT and Integer MDCT algorithm and to find which algorithm shows better Compression Ratio(CR).The confines of this task is to hybriding lossy and lossless audio codec with diminished bit rate but with finer sound quality. Certainly the quality of the audio is figure out by Subjective and Objective testing which is in terms of MOS (Mean opinion square) , ABx and some of the hearing aid testing methodology like PEAQ(Perceptual Evaluation Audio Quality) and ODG(Objective Difference Grade)is followed. Execution measure, that is Compression Ratio(CR) and Sound Pressure Level (SPL) is approximated.

Adaptive Cross-Correlation Compression Method in Lossless Audio Streaming Compression

Adaptive Cross-Correlation Compression Method in Lossless Audio Streaming Compression

Detection of Genuine Lossless Audio Files: Application to the MPEG-AAC Codec

In this study, we describe an algorithm that discriminates transcoded from true lossless audio files, i.e. file that are obtained by directly ripping a genuine audio CD, from file that are decoded from a lossy audio format (e.g. MP3, AAC) and re-encoded with a lossless format. This method should allow both consumers and online music dealers to check the authenticity of lossless music files. This implementation focuses on the MPEG AAC codec, but the method can also be applied to MP3 codec. It is based on the detection of quantization errors in the time-frequency domain, without machine learning. The final results on a large database of 1576 audio files, original and transcoded with AAC from iTunes, show null false positive ratios and very low false negative ratios, possibly null for high-precision settings, which is better than other methods.

A Critical Assessment of Advanced Coding Standards for Lossless Audio Compression

A Critical Assessment of Advanced Coding Standards for Lossless Audio Compression

Comparison of Entropy Coding mechanism on IEEE1857.2 Lossless Audio Compression Standard

<p>This paper has two objectives. First, we aim to review and analyze the performance of the IEEE 1857.2 standard, focusing is on the Golomb-Rice and Arithmetic entropy algorithms as well as the effect of the pre-processing block on these entropy blocks. The pre-processing block normalizes the error residue of the Linear Predictive encoder, which then is passed to Entropy block, where the selector chooses the entropy encoder to use. The second objective is to present results from experimenting different existing algorithms available to benchmark it’. The results are discussed, and comparisons are made to identify the effect on compression ratio and encoding speed of the lossless encoder. As well as this, comparison is made to analyze effects of enabling and disabling the pre-processing output to the Entropy Coding block. We concluded that pre-processing block works well to flatten the output at lower predictor order for all the sound types, but works best at improving the residual output for music sound type.</p>

Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

As majority of the compression algorithms are implementations for CPU architecture, the primary focus of our work was to exploit the opportunities of GPU parallelism in audio compression. This paper presents an implementation of Apple Lossless Audio Codec (ALAC) algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. The core idea was to identify the areas where data parallelism could be applied and parallel programming model CUDA could be used to execute the identified parallel components on Single Instruction Multiple Thread (SIMT) model of CUDA. The dataset was retrieved from European Broadcasting Union, Sound Quality Assessment Material (SQAM). Faster execution of the algorithm led to execution time reduction when applied to audio coding for large audios. This paper also presents the reduction of power usage due to running the parallel components on GPU. Experimental results reveal that we achieve about 80-90% speedup through CUDA on the identified components over its CPU implementation while saving CPU power consumption.

Investigation of Lossless Audio Compression using IEEE 1857.2 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>

Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain

Robust and reversible watermarking is a potential technique in many sensitive applications, such as lossless audio or medical image systems. This paper presents a novel robust reversible audio watermarking method by modifying the statistic features in time domain in the way that the histogram of these statistical values is shifted for data hiding. Firstly, the original audio is divided into nonoverlapped equal-sized frames. In each frame, the use of three samples as a group generates a prediction error and a statistical feature value is calculated as the sum of all the prediction errors in the frame. The watermark bits are embedded into the frames by shifting the histogram of the statistical features. The watermark is reversible and robust to common signal processing operations. Experimental results have shown that the proposed method not only is reversible but also achieves satisfactory robustness to MP3 compression of 64 kbps and additive Gaussian noise of 35 dB.