Fast fundamental frequency determination via adaptive autocorrelation

Abstract

We present an algorithm for the estimation of fundamental frequencies in voiced audio signals. The method is based on an autocorrelation of a signal with a segment of the same signal. During operation, frequency estimates are calculated and the segment is updated whenever a period of the signal is detected. The fast estimation of fundamental frequencies with low error rate and simple implementation is interesting for real-time speech signal processing.

PDMAA Hydrogel Coated U-Bend Humidity Sensor Suited for Mass-Production

“…To investigate the stability of the PDMAA cladding, we repeated the measurements with the same sensors after 4 weeks, finding no discernible difference in sensitivity or reaction time. To automate the process of measuring the breathing frequency during the measurement as would be desirable in a clinical environment for monitoring a patient’s breathing frequency (e.g., to detect the onset of panic during an MRI measurement), we utilized a new read-out approach initially developed in the field of speech signal processing [18]. …”

“…Looking into the field of speech signal processing, Staudacher et al proposed a method to quickly determine the fundamental frequency of a speech signal using an adaptive autocorrelation approach [18]. This approach is meant to quickly identify the pitch of a speech signal (e.g., to detect questions, which normally end with a rise in pitch towards the end of the sentence) in a real-time speech processing application.…”

“…( a ) Measurement data over 200 s showing regular breathing and ( b ) fast adaptive evaluation of the breathing frequency using an audio processing approach [18]. …”

A method for estimation of fundamental frequency for tonal sounds inspired on bird song studies

“…In the human case, one goal in many speech analysis applications is to follow fast variations in the fundamental frequency (f 0 ) of a signal. Again, several studies were conducted in this field [[4], [5], [6], [7]].…”

“…To perform this task, there are currently a lot of methods. In the case of speech, many pitch detection algorithms (PDAs) analyze a speech signal by partitioning it into segments and calculating the respective fundamental frequencies (short-term analysis) [4].…”