Noise robust F0 determination and epoch-marking algorithms

Kotnik, Bojan; Höge, Harald; Kačič, Zdravko

doi:10.1016/j.sigpro.2009.04.017

Cited by 9 publications

(12 citation statements)

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“…Since a decade, there have been some performance evaluations conducted either on simulated noisy data (e.g., [14], where noise is added to clean speech reference signals) or on real noisy data using the SPEECON corpus (e.g., [15]- [17]). However, only a small set of pitch detection algorithms were considered in each case.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of several pitch detection algorithms on simulated and real noisy speech data

Jouvet

Laprie

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper analyses the performance of a large bunch of pitch detection algorithms on clean and noisy speech data. Two sets of noisy speech data are considered. One corresponds to simulated noisy data, and is obtained by adding several types of noise signals at various levels on the clean speech data of the Pitch-Tracking Database from Graz University of Technology (PTDB-TUG). The second one, SPEECON, was recorded in several different acoustic environments. The paper discusses the performance of pitch detection algorithms on the simulated noisy data, and on the real noisy data of the SPEECON corpus. Also, an analysis of the performance of the best pitch detection algorithm with respect to estimated signal-to-noise ratio (SNR) shows that very similar performance is observed on the real noisy data recorded in public places, and on the clean data with addition of babble noise.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of several pitch detection algorithms on simulated and real noisy speech data

Jouvet

Laprie

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Vocal cords within the laryngeal structure vibrate due to air passing through them during voiced speech (Swee et al, 2010). During voiced phonation pitch is produced and the fundamental frequency, F 0 and its reciprocal known as pitch period, T 0 can be calculated (Amado and Filho, 2008;Kotnik et al, 2009;Manfredi et al, 2000). Vocal hyperfunction, vocal abuse and misuse, or unhealthy social habits such as smoking and alcohol consumption may over time, cause physical changes to the laryngeal structure and lead to voice changes such as loss of power, changes in pitch and reduction in voice range (Hadjitodorov and Mitev, 2002;Timmermans et al, 2002;Godino-Llorente et al, 2006;De Bodt et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The results were compared with the datasheet provided by KayPENTAX Elemetrics for the accuracy test. The performance of the proposed PDA was also compared with the well-known and publicly available PRAAT toolkit (Kotnik et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…From these basic time domain PDA's, many researchers have modified these algorithms so that it will work more efficiently to obtain pitch. One of the interesting approaches was Merged Normalized Forward Backward Correlation (MNFBC) which basically used the same concept of autocorrelation but instead of using autocorrelation, it uses MNFBC which is to be noise robust (Kotnik et al, 2009). Plus the method of finding the exact pitch period was by implementing viterbi search to the MNFBC.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Time Domain Pitch Detection Algorithm for Pathological Voice

Antonios¹

2012

American Journal of Applied Sciences

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Problem statement: The present study proposes a new pitch detection algorithm which could potentially be used to detect pitch for disordered or pathological voices. One of the parameters required for dysphonia diagnosis is pitch and this prompted the development of a new and reliable pitch detection algorithm capable of accurately detect pitch in disordered voices. Approach: The proposed method applies a technique where the frame size of the half wave rectified autocorrelation is adjusted to a smaller frame after two potential pitch candidates are identified within the preliminary frame. Results: The method is compared to PRAATs standard autocorrelation and the result shows a significant improvement in detecting pitch for pathological voices. Conclusion: The proposed method is more reliable way to detect pitch, either in low or high pitched voice without adjusting the window size, fixing the pitch candidate search range and predefining threshold like most of the standard autocorrelation do

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“…Current approaches on pitch marking are mainly focused on obtaining reliable results in the presence of noise [21]- [23]. However, when labeling large clean speech for synthesis purposes, the pitch marking algorithm is challenged by the acoustic characteristics of affective speech (e.g., larger variability than neutral speech) [13].…”

Section: Introductionmentioning

confidence: 99%

Reliable Pitch Marking of Affective Speech at Peaks or Valleys Using Restricted Dynamic Programming

Álías

Munné

2010

IEEE Trans. Multimedia

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The affective communication channel plays a key role in multimodal human-computer interaction. In this context, the generation of realistic talking-heads expressing emotions both in appearance and speech is of great interest. The synthetic speech of talking-heads is generally obtained from a text-to-speech (TTS) synthesizer. One of the dominant techniques for achieving high-quality synthetic speech is unit-selection TTS (US-TTS) synthesis. Affective US-TTS systems are driven by affective annotated speech databases. Since affective speech involves higher acoustic variability than neutral speech, achieving trustworthy speech labeling is a more challenging task. To that effect, this paper introduces a methodology for achieving reliable pitch marking on affective speech. The proposal adjusts the pitch marks at the signal peaks or valleys after applying a three-stage restricted dynamic programming algorithm. The methodology can be applied as a post-processing of any pitch determination and pitch marking algorithm (with any local criterion for locating pitch marks), or their merging. The experiments show that the proposed methodology significantly improves the results of the input state-of-the-art markers on affective speech.Index Terms-Affective speech, dynamic programming, pitch marking, speech analysis, unit-selection text-to-speech synthesis.

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Noise robust F0 determination and epoch-marking algorithms

Cited by 9 publications

References 13 publications

Performance analysis of several pitch detection algorithms on simulated and real noisy speech data

Performance analysis of several pitch detection algorithms on simulated and real noisy speech data

An Improved Time Domain Pitch Detection Algorithm for Pathological Voice

Reliable Pitch Marking of Affective Speech at Peaks or Valleys Using Restricted Dynamic Programming

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