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DWS pitch detection algorithm extended to the time domain

Lei Lf Willems

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Abstract

The DWS pitch detection algorithm finds the best harmonic relation among the peaks\nin the amplitude spectrum of the acoustic signal. It is argued that, for high-pitch\nsignals, there is relatively little information available in the spectrum compared\nto the amount of temporal information that can be obtained from the autocorrelation\nfunction, for instance. This paper describes how the DWS algorithm was applied to\nthe autocorrelation function and how the results from the frequency and time domains\nwere combined to obtain a more reliable pitch estimate.

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What this paper is about

The DWS pitch detection algorithm finds the best harmonic relation among the peaks\nin the amplitude spectrum of the acoustic signal. It is argued that, for high-pitch\nsignals, there is relatively little information available in the spectrum compared\nto the amount of temporal information that can be obtained from the autocorrelation\nfunction, for instance. This paper describes how the DWS algorithm was applied to\nthe autocorrelation function and how the results from the frequency and time domains\nwere combined to obtain a more reliable pitch estimate.

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Available abstract

The DWS pitch detection algorithm finds the best harmonic relation among the peaks\nin the amplitude spectrum of the acoustic signal. It is argued that, for high-pitch\nsignals, there is relatively little information available in the spectrum compared\nto the amount of temporal information that can be obtained from the autocorrelation\nfunction, for instance. This paper describes how the DWS algorithm was applied to\nthe autocorrelation function and how the results from the frequency and time domains\nwere combined to obtain a more reliable pitch estimate.

Key concepts: Autocorrelation, Pitch detection algorithm, SIGNAL (programming language), Time domain, Amplitude, Harmonic, Algorithm, Autocorrelation technique

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