Estimation of Severity of Speech Disability through Speech Envelope

In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC

Model-Based Separation in Humans and Machines

Comparing human performance on source separation with different automatic approaches, and arguing for (a) using models, and (b) concentrating on the content, not the signal per se

Speech Separation in Humans and Machines

An overview of the problem of separating speech in acoustic mixtures, including some perceptual results, brief introductions to ICA and CASA, and a pitch for model-based analysis

Single channel speech separation with a frame-based pitch range estimation method in modulation frequency

Computational Auditory Scene Analysis (CASA) has attracted a lot of interest in segregating speech from monaural mixtures. In this paper, we propose a new method for single channel speech separation with frame-based pitch range estimation in modulation frequency domain. This range is estimated in each frame of modulation spectrum of speech by analyzing onsets and offsets. In the proposed method, target speaker is separated from interfering speaker by filtering the mixture signal with a mask extracted from the modulation spectrogram of mixture signal. Systematic evaluation shows an acceptable level of separation comparing with classic methods

Auditory Scene Analysis in Humans and Machines

Tutorial on auditory scene analysis and source separation in humans and machines

CPU consumption for AM/FM audio effects

In this paper we present an assessment of the computational performance regarding the use of the AM/FM decomposition framework for the design and implementation of audio effects. The equations and intuitions are reviewed and audio examples are provided, alongside Csound code for real-time implementation. Two types of hardware and several computer music techniques were considered for the comparisons. We also introduce sqENVerb, a novel inexpensive reverb-enhancer effect