We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on systemperformanceand the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound. 1. OVERVIEW The problem of distinguishing speech signals from music signals has become increasingly important as automatic speech recognition (ASR) systems are applied to more and more "real-world" multimedia domains. If we wish to build systems that perform ASR on soundtrack data, for example, it is important to be able to distinguish which segments...

Monaural sound source separation is the process of separating sound sources from a single channel mixture. In mixtures of pitched musical instru-ments, the problem of overlapping harmonics poses a significant challenge to source separation and reconstruction. One standard method to resolve over

This thesis concerns the problem of automatic transcription of music and proposes a method for transcribing monophonic melodies. Recently, computational music content analysis has received considerable attention among researchers due to the rapid growth of music databases. In this area of research

Understanding the underlying intentions of a music per-former is crucial to enable a machine such as an automated accompaniment system to interact intelligently with a musi-cian. Particularly, understanding the symbol associated with

of coding is very well suited for intuitively and efficiently representing magnitude spectra. We present results that reveal the nature of these basis functions and we introduce their utility in separating monophonic mixtures of known speakers.

We are looking for a mathematical model of monophonic sounds with independent time and phase dimensions. With such a model we can resynthesise a sound with arbitrarily modulated frequency and progress of the timbre. We propose such a model and show that it exactly fulfils some natural properties

Introduction In blind source separation an N-channel sensor signal x(t) arises from M unknown scalar source signals s i (t), linearly mixed together by an unknown N M matrix A, and possibly corrupted by additive noise (t) x(t) = As(t) + (t) (1.1) We wish to estimate the mixing matrix A and the M-dimensional source signal s(t). Many natural signals can be sparsely represented in a proper signal dictionary s i (t) = K X k=1 C ik ' k (t) (1.2) The scalar functions ' k

Abstract not found

In this paper, we propose a novel confidence scoring method that is applied to N-best hypotheses output from an HMM-based classifier. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses (word candidates) and boundaries of all

) and perceptual linear prediction (PLP) both are used as a feature extraction techniques in our proposed system. Monophone based acoustic modeling is done by Hidden Markov Model (HMM) at the back-end of an ASR system. HTK 3.4.1 toolkit is used for the implementation of this system. The system is trained for 70