Frequency-warped autoregressive modeling and filtering

This thesis consists of an introduction and nine articles. The articles are related to the application of frequency-warping techniques to audio signal processing, and in particular, predictive coding of wideband audio signals. The introduction reviews the literature and summarizes the results of the articles. Frequency-warping, or simply warping techniques are based on a modification of a conventional signal processing system so that the inherent frequency representation in the system is changed. It is demonstrated that this may be done for basically all traditional signal processing algorithms. In audio applications it is beneficial to modify the system so that the new frequency representation is close to that of human hearing. One of the articles is a tutorial paper on the use of warping techniques in audio applications. Majority of the articles studies warped linear prediction, WLP, and its use in wideband audio coding. It is proposed that warped linear prediction would be particularly attractive method for low-delay wideband audio coding. Warping techniques are also applied to various modifications of classical linear predictive coding techniques. This was made possible partly by the introduction of a class of new implementation techniques for recursive filters in one of the articles. The proposed implementation algorithm for recursive filters having delay-free loops is a generic technique. This inspired to write an article which introduces a generalized warped linear predictive coding scheme. One example of the generalized approach is a linear predictive algorithm using almost logarithmic frequency representation.reviewe

Motion estimation and signaling techniques for 2D+t scalable video coding

We describe a fully scalable wavelet-based 2D+t (in-band) video coding architecture. We propose new coding tools specifically designed for this framework aimed at two goals: reduce the computational complexity at the encoder without sacrificing compression; improve the coding efficiency, especially at low bitrates. To this end, we focus our attention on motion estimation and motion vector encoding. We propose a fast motion estimation algorithm that works in the wavelet domain and exploits the geometrical properties of the wavelet subbands. We show that the computational complexity grows linearly with the size of the search window, yet approaching the performance of a full search strategy. We extend the proposed motion estimation algorithm to work with blocks of variable sizes, in order to better capture local motion characteristics, thus improving in terms of rate-distortion behavior. Given this motion field representation, we propose a motion vector coding algorithm that allows to adaptively scale the motion bit budget according to the target bitrate, improving the coding efficiency at low bitrates. Finally, we show how to optimally scale the motion field when the sequence is decoded at reduced spatial resolution. Experimental results illustrate the advantages of each individual coding tool presented in this paper. Based on these simulations, we define the best configuration of coding parameters and we compare the proposed codec with MC-EZBC, a widely used reference codec implementing the t+2D framework

Audio Compression using a Modified Vector Quantization algorithm for Mastering Applications

Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms

Structure-Constrained Basis Pursuit for Compressively Sensing Speech

Compressed Sensing (CS) exploits the sparsity of many signals to enable sampling below the Nyquist rate. If the original signal is sufficiently sparse, the Basis Pursuit (BP) algorithm will perfectly reconstruct the original signal. Unfortunately many signals that intuitively appear sparse do not meet the threshold for sufficient sparsity . These signals require so many CS samples for accurate reconstruction that the advantages of CS disappear. This is because Basis Pursuit/Basis Pursuit Denoising only models sparsity. We developed a Structure-Constrained Basis Pursuit that models the structure of somewhat sparse signals as upper and lower bound constraints on the Basis Pursuit Denoising solution. We applied it to speech, which seems sparse but does not compress well with CS, and gained improved quality over Basis Pursuit Denoising. When a single parameter (i.e. the phone) is encoded, Normalized Mean Squared Error (NMSE) decreases by between 16.2% and 1.00% when sampling with CS between 1/10 and 1/2 the Nyquist rate, respectively. When bounds are coded as a sum of Gaussians, NMSE decreases between 28.5% and 21.6% in the same range. SCBP can be applied to any somewhat sparse signal with a predictable structure to enable improved reconstruction quality with the same number of samples

Feature extraction of musical content for automatic music transcription

The purpose of this thesis is to develop new methods for automatic transcription of melody and harmonic parts of real-life music signal. Music transcription is here defined as an act of analyzing a piece of music signal and writing down the parameter representations, which indicate the pitch, onset time and duration of each pitch, loudness and instrument applied in the analyzed music signal. The proposed algorithms and methods aim at resolving two key sub-problems in automatic music transcription: music onset detection and polyphonic pitch estimation. There are three original contributions in this thesis. The first is an original frequency-dependent time-frequency analysis tool called the Resonator Time-Frequency Image (RTFI). By simply defining a parameterized function mapping frequency to the exponent decay factor of the complex resonator filter bank, the RTFI can easily and flexibly implement the time-frequency analysis with different time-frequency resolutions such as ear-like (similar to human ear frequency analyzer), constant-Q or uniform (evenly-spaced) time-frequency resolutions. The corresponding multi-resolution fast implementation of RTFI has also been developed. The second original contribution consists of two new music onset detection algorithms: Energy-based detection algorithm and Pitch-based detection algorithm. The Energy-based detection algorithm performs well on the detection of hard onsets. The Pitch-based detection algorithm is the first one, which successfully exploits the pitch change clue for the onset detection in real polyphonic music, and achieves a much better performance than the other existing detection algorithms for the detection of soft onsets. The third contribution is the development of two new polyphonic pitch estimation methods. They are based on the RTFI analysis. The first proposed estimation method mainly makes best of the harmonic relation and spectral smoothing principle, consequently achieves an excellent performance on the real polyphonic music signals. The second proposed polyphonic pitch estimation method is based on the combination of signal processing and machine learning. The basic idea behind this method is to transform the polyphonic pitch estimation as a pattern recognition problem. The proposed estimation method is mainly composed by a signal processing block followed by a learning machine. Multi-resolution fast RTFI analysis is used as a signal processing component, and support vector machine (SVM) is selected as learning machine. The experimental result of the first approach show clear improvement versus the other state of the art methods

A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed