Estimation of Fundamental Frequency from Singing Voice Using Harmonics of Impulse-like Excitation Source

Abstract

This paper focuses on the problem of estimating fundamental frequency from singing voice. Estimation of fundamental frequency is a well studied topic in the speech research community. From the recent studies on fundamental frequency estimation from singing voice with state-of-art methods proposed for speech, there exists a significant gap in accuracy for singing voice. This is mainly because of the wider and rapid variations in pitch in singing voice compared to that in speech. To overcome this, in this paper we propose a method to derive the fundamental frequency from singing voice by exploiting the harmonics of impulse-like excitation in sequence of glottal cycles. The proposed method is compared with the eight state-of-art methods such as YIN, SWIPE, YAAPT, RAPT, SRH, SFF CEP, PEFAC and SHRP on the LYRICS singing database. From the experimental results, it is observed that the accuracy of fundamental frequency by the proposed method is better than many state-of-art methods in various singing categories and laryngeal mechanisms.

Extraction and Utilization of Excitation Information of Speech: A Review

“…As F0 extraction is covered in several tutorials/books, this topic is not handled in detail in this review article, but we, instead, discuss the general aspects of F0 extraction briefly here and focus more on recent deep learning-based progress of the topic in Section VI-A. For more details on F0 extraction, please see [96]- [104], where various methods are reviewed by for the study of clean and noisy speech, as well as singing voices.…”

“…This property forms the basis for frequencydomain methods. Examples of methods belonging to this category are the SHRP [110], the SRH [111], the summation of impulse-sequence harmonics [104], the method of dominant harmonics [112], and the SWIPE [113].…”

“…The time instant of the negative peak is called GCI. The GCI is used in different areas of speech research, such as study of glottal activity [116], estimation of pitch [104], [117]- [119] and formants [120], [121], and the analysis of loudness [36] and nonverbal sounds (such as laughter [23] and shouting [122]). GCIs are also used in the time delay estimation [123]- [125], in determining the number of speakers from mixed signals [126], speech enhancement [5], [6], multispeaker separation [127], prosody modification [128], and speech synthesis [3], [4].…”

“…In a more recent study [158], a method was proposed to extract a sequence of impulses from the signal by modifying the ZFF method using various levels of trend removals. This approach is justified by the pitch perception of expressive voices [104], [161]- [163]. However, there is a need for signal processing techniques that can exploit the impulse-like sequences derived directly from the input signal without using block processing and vocal tract system characteristics.…”

A Quantitative Comparison of Epoch Extraction Algorithms for Telephone Speech

“…Identification of epoch locations plays a crucial role in many speech processing applications such as speech modification [1], excitation source modeling [2], inverse filtering [3,4], joint optimization in concatenative speech synthesis [5], speech pathology [6,7], etc. Apart from above applications, the high SNR property of the GCI was used in applications like glottal activity detection [8], pitch tracking [9,10], formant frequencies [11], analysis and detection of phonation types [12,13] and emotions [14,15], speaker recognition [16], speech enhancement [8], multi-speaker separation, identification of number of speakers from multi-speakers data [17] etc. Due to wider range of applications, GCI detection has received a considerable amount of research attention.…”