Real-time digital hardware pitch detector

Dubnowski, J.; Schafer, Ronald W.; Rabiner, L. R.

doi:10.1109/tassp.1976.1162765

Cited by 132 publications

(54 citation statements)

References 11 publications

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“…Besides providing valuable insights into the nature of the excitation source for speech production, the pitch contour of an utterance is useful for recognizing speakers [1], [2], for speech instruction to the hearing impaired [31, and is required in almost all speech analysis-synthesis (vocoder) systems [4] Because of the importance of pitch detection, a wide variety of algorithms for pitch detection have been proposed in the speech processing literature (e.g., [5] - [11]). In spite of the proliferation of pitch detectors, very little formal evaluation and comparison among the different types of pitch detectors has been attempted.…”

Section: Introductionmentioning

confidence: 99%

A comparative performance study of several pitch detection algorithms

Rabiner¹,

Cheng²,

Rosenberg³

et al. 1976

IEEE Trans. Acoust., Speech, Signal Process.

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Abstract-A comparative performance study of seven pitch detection algorithms was conducted. A speech data base, consisting of eight utterances spoken by three males, three females, and one child was constructed. Telephone, close talking microphone, and wideband recordings were made of each of the utterances. For each of the utterances in the data base; a "standard" pitch contour was semiautomatically measured using a highly sophisticated interactive pitch detection program. The "standard" pitch contour was then compared with the pitch contour that was obtained from each of the seven programmed pitch detectors. The algorithms used in this study were 1) a center clipping, infinite-peak clipping, modified autocorrelation method (AUTOC), 2) the cepstral method (CEP), 3) the simplified inverse filtering technique (SIFT) method, 4) the parallel processing time-domain method (PPROC), 5) the data reduction method (DARD), 6) a spectral flattening linear predictive coding (LPC) method, and 7) the average magnitude difference function (AMDF) method. A set of measurements was made on the pitch contours to quantify the various types of errors which occur in each of the above methods. Included among the error measurements were the average and standard deviation of the error in pitch period during voiced regions, the number of gross errors in the pitch period, and the average number of voiced-unvoiced classification errors. For each of the error measurements, the individual pitch detectors could be rank ordered as a measure of their relative performance as a function of recording condition, and pitch range of the various speakers. Performance scores are presented for each of the seven pitch detectors based on each of the categories of error.

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Section: Introductionmentioning

confidence: 99%

A comparative performance study of several pitch detection algorithms

Rabiner¹,

Cheng²,

Rosenberg³

et al. 1976

IEEE Trans. Acoust., Speech, Signal Process.

Self Cite

586

257

View full text Add to dashboard Cite

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“…Simply put, it ensures that the remaining samples either side of zero were intended to be of that polarity owing to their original distance from zero. The boundaries are a set percentage of the peak signal amplitude, varying from 30% (Upadhya, 2012) to 80% (Dubnowski, Schafer, & Rabiner, 1976) in different studies.…”

Section: Algorithms and Categorisationmentioning

confidence: 99%

“…Centre-clipping boundary (for MACF and IACF): This was expressed as a percentage of the lower of the absolute positive and negative waveform peaks following the methodology of Dubnowski et al (1976).…”

Section: Sampling Frequency (Fs)mentioning

confidence: 99%

Minimising latency of pitch detection algorithms for live vocals on low-cost hardware

Firth

2016

Fields: Journal of Huddersfield Student Research

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A pitch estimation device was proposed for live vocals to output appropriate pitch data through the musical instrument digital interface (MIDI). The intention was to ideally achieve unnoticeable latency while maintaining estimation accuracy. The projected target platform was low-cost, standalone hardware based around a microcontroller such as the Microchip PIC series. This study investigated, optimised and compared the performance of suitable algorithms for this application.Performance was determined by two key factors: accuracy and latency. Many papers have been published over the past six decades assessing and comparing the accuracy of pitch detection algorithms on various signals, including vocals. However, very little information is available concerning the latency of pitch detection algorithms and methods with which this can be minimised. Real-time audio introduces a further latency challenge that is sparsely studied, minimising the length of sampled audio required by the algorithms in order to reduce overall total latency.Thorough testing was undertaken in order to determine the best-performing algorithm and optimal parameter combination. Software modifications were implemented to facilitate accurate, repeatable, automated testing in order to build a comprehensive set of results encompassing a wide range of test conditions.The results revealed that the infinite-peak-clipping autocorrelation function (IACF) performed better than the other autocorrelation functions tested and also identified ideal parameter values or value ranges to provide the optimal latency/accuracy balance.Although the results were encouraging, testing highlighted some fundamental issues with vocal pitch detection. Potential solutions are proposed for further development.

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“…A software package was developed in Matlab for processing the speech signal and graphical display of the recorded and selected speech segments, pitch and energy contours, spectrogram, and areagram. The pitch was estimated by the short-time autocorrelation method [33], with energy as zeroth autocorrelation coefficient. The package was tested by analyzing sustained vowel and VCV utterances to check consistency and validity of the estimated shapes.…”

Section: Investigation Of Lpc-based Vocal Tract Shape Estimationmentioning

confidence: 99%