Download Discrete Implementation Of The First Order System Cascade As The Basis For A Melodic Segmentation Model The basis for a low-level melodic segmentation model and its discrete implementation is presented. The model is based on the discrete approximation of the one-dimensional convective transport mechanism. In this way, a physically plausible mechanism for achieving multi-scale representation is obtained. Some aspects of edge detection theory thought to be relevant for solving similar problems in auditory perception are briefly introduced. Two examples presenting the dynamic behaviour of the model are shown.
Download Sound Source Separation: Preprocessing For Hearing Aids And Structured Audio Codin In this paper we consider the problem of separating different sound sources in multichannel audio signals. Different approaches to the problem of Blind Source Separation (BSS), e.g. the Independent Component Analysis (ICA) originally proposed by Herault and Jutten, and extensions to this including delays, work fine for artificially mixed signals. However the quality of the separated signals is severely degraded for real sound recordings when there is reverberation. We consider the system with 2 sources and 2 sensors, and show how we can improve the quality of the separation by a simple model of the audio scene. More specifically we estimate the delays between the sensor signals, and put constraints on the deconvolution coefficients.
Download An Efficient Pitch-Tracking Algorithm Using A Combination Of Fourier Transforms In this paper we present a technique for detecting the pitch of sound using a series of two forward Fourier transforms. We use an enhanced version of the Fourier transform for a better accuracy, as well as a tracking strategy among pitch candidates for an increased robustness. This efficient technique allows us to precisely find out the pitches of harmonic sounds such as the voice or classic musical instruments, but also of more complex sounds like rippled noises.
Download Automating The Design Of Sound Synthesis Techniques Using Evolutionary Methods Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its functional form and internal parameters. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of inputs + target sound. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples. Design of a SST is stated as a search problem in the SST space (the space spanned by all the possible valid functional forms and internal parameters, within certain limits to make it practical). This search is done using evolutionary methods; specifically, Genetic Programming (GP).
Download Classification Of Music Signals In The Visual Domain With the huge increase in the availability of digital music, it has become more important to automate the task of querying a database of musical pieces. At the same time, a computational solution of this task might give us an insight into how humans perceive and classify music. In this paper, we discuss our attempts to classify music into three broad categories: rock, classical and jazz. We discuss the feature extraction process and the particular choice of features that we used- spectrograms and mel scaled cepstral coefficients (MFCC). We use the texture-of- texture models to generate feature vectors out of these. Together, these features are capable of capturing the frequency-power profile of the sound as the song proceeds. Finally, we attempt to classify the generated data using a variety of classifiers. we discuss our results and the inferences that can be drawn from them.
Download Separation Of Speech Signal From Complex Auditory Scenes The hearing system, even in front of complex auditory scenes and in unfavourable conditions, is able to separate and recognize auditory events accurately. A great deal of effort has gone into the understanding of how, after having captured the acoustical data, the human auditory system processes them. The aim of this work is the digital implementation of the decomposition of a complex sound in separate parts as it would appear to a listener. This operation is called signal separation. In this work, the separation of speech signal from complex auditory scenes has been studied and an experimentation of the techniques that address this problem has been done.