Advances in Similarity-Based Audio Compression

Existing lossy audio compression techniques such as MP3, WMA and Ogg Vorbis, for example, demonstrate great success in providing compression ratios which successfully reduce the data size from the original sampled audio. These techniques employ psychoacoustic models and traditional statistical coding techniques to achieve data reduction. However, these methods do not take into account the perceived content of the audio, which is often particularly relevant in musical audio. In this paper, we present our research and development work completed to date, in producing a system for audio analysis, which will consider and exploit the repetitive nature of audio and the similarities which frequently occur in audio recordings. We demonstrate the feasibility and scope of the analysis system and consider the techniques and challenges that are employed to achieve data reduction

Comparação entre os algoritmos de codificação Huffman e Lempel-Ziv para compressão de sinais de áudio após a filtragem

The Huffman and Lempel-Ziv coding algorithms are extensively used in digital communications for data compression. Through this data compression it is possible to significantly decrease the file size, reducing data storage costs and making the systems faster and more efficient. In this work, it is studied which of these algorithms has the best compression performance for filtered and unfiltered audio signals. The metrics used to analyze each performance are: signal-to-noise ratio, average code length and compression ratio. Moreover, Matlab software is used to simulate the distinct scenarios presented here.Trabalho de Conclusão de Curso (Graduação)Os algoritmos de codificação Huffman e Lempel-Ziv são extensivamente utilizados em comunicações digitais para a compressão de dados. Por meio dessa compressão é possível reduzir significativamente o tamanho dos arquivos, possibilitando a redução de gastos com armazenamento de dados e tornando os sistemas mais rápidos e eficientes. Neste trabalho é estudado qual desses algoritmos tem o melhor desempenho de compressão para sinais de áudio filtrados e não filtrados. As métricas utilizadas para a análise de cada desempenho são: relação sinal ruído, comprimento médio de código e taxa de compressão. Em função disso, é utilizado o software Matlab para a simulação dos diferentes panoramas aqui apresentados

Generalized Lempel-Ziv Compression for Audio

We introduce a novel compression paradigm to generalize a class of Lempel-Ziv algorithms for lossy compression of multimedia. Based upon the fact that music, in particular electronically generated sound, has substantial level of repetitiveness within a single clip, we generalize the basic Lempel-Ziv compression algorithm to support representing a single window of audio using a linear combination of filtered past windows. In this positioning paper, we present a detailed overview of the new compression paradigm, we identify the basic challenges such as similarity search and present preliminary experimental results on a benchmark of electronically generated musical pieces