Scalable and perceptual audio compression

This thesis deals with scalable perceptual audio compression. Two scalable perceptual solutions as well as a scalable to lossless solution are proposed and investigated. One of the scalable perceptual solutions is built around sinusoidal modelling of the audio signal whilst the other is built on a transform coding paradigm. The scalable coders are shown to scale both in a waveform matching manner as well as a psychoacoustic manner. In order to measure the psychoacoustic scalability of the systems investigated in this thesis, the similarity between the original signal\u27s psychoacoustic parameters and that of the synthesized signal are compared. The psychoacoustic parameters used are loudness, sharpness, tonahty and roughness. This analysis technique is a novel method used in this thesis and it allows an insight into the perceptual distortion that has been introduced by any coder analyzed in this manner

Non-intrusive identification of speech codecs in digital audio signals

Speech compression has become an integral component in all modern telecommunications networks. Numerous codecs have been developed and deployed for efficiently transmitting voice signals while maintaining high perceptual quality. Because of the diversity of speech codecs used by different carriers and networks, the ability to distinguish between different codecs lends itself to a wide variety of practical applications, including determining call provenance, enhancing network diagnostic metrics, and improving automated speaker recognition. However, few research efforts have attempted to provide a methodology for identifying amongst speech codecs in an audio signal. In this research, we demonstrate a novel approach for accurately determining the presence of several contemporary speech codecs in a non-intrusive manner. The methodology developed in this research demonstrates techniques for analyzing an audio signal such that the subtle noise components introduced by the codec processing are accentuated while most of the original speech content is eliminated. Using these techniques, an audio signal may be profiled to gather a set of values that effectively characterize the codec present in the signal. This procedure is first applied to a large data set of audio signals from known codecs to develop a set of trained profiles. Thereafter, signals from unknown codecs may be similarly profiled, and the profiles compared to each of the known training profiles in order to decide which codec is the best match with the unknown signal. Overall, the proposed strategy generates extremely favorable results, with codecs being identified correctly in nearly 95% of all test signals. In addition, the profiling process is shown to require a very short analysis length of less than 4 seconds of audio to achieve these results. Both the identification rate and the small analysis window represent dramatic improvements over previous efforts in speech codec identification

Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.