Pitch Estimation Method Research Articles

An improvedsub‐harmonicto harmonic ratio method for pitch estimation and Shadja detection

SummaryIn this paper, we have presented an improved Sub‐Harmonic to Harmonic ratio (SHR) algorithm using the Genetic algorithm (GA) for pitch estimation of audio recordings of various ragas. Then we study the problem of Shadja identification using machine learning with the help of feature extraction and classification. The extraction of features from the raga signal is done with the help of statistical analysis of pitch estimation and these features are classified using a Neural Network (NN). Here, the training of NN is accomplished by Gray Wolf Optimization (GWO) algorithm for, determining the weights. Performance of the proposed Sa detection algorithm is analyzed by comparing the developed NN using Gray Wolf Optimization (GWO) models with the conventional models such as Levenberg Marquardt based NN (LM‐NN), Gradient Descent based (GD‐NN), Particle Swarm Optimization based NN (PSO‐NN) and FireFly based NN (FF‐NN) in terms of positive and negative performance measures.

A Robust and Low Computational Cost Pitch Estimation Method.

Pitch estimation is widely used in speech and audio signal processing. However, the current methods of modeling harmonic structure used for pitch estimation cannot always match the harmonic distribution of actual signals. Due to the structure of vocal tract, the acoustic nature of musical equipment, and the spectrum leakage issue, speech and audio signals’ harmonic frequencies often slightly deviate from the integer multiple of the pitch. This paper starts with the summation of residual harmonics (SRH) method and makes two main modifications. First, the spectral peak position constraint of strict integer multiple is modified to allow slight deviation, which benefits capturing harmonics. Second, a main pitch segment extension scheme with low computational cost feature is proposed to utilize the smooth prior of pitch more efficiently. Besides, the pitch segment extension scheme is also integrated into the SRH method’s voiced/unvoiced decision to reduce short-term errors. Accuracy comparison experiments with ten pitch estimation methods show that the proposed method has better overall accuracy and robustness. Time cost experiments show that the time cost of the proposed method reduces to around 1/8 of the state-of-the-art fast NLS method on the experimental computer.

Steelpan fundamental frequency estimation through audio feature extraction and deep neural networks

The estimation of fundamental frequency, or pitch, is a fundamental task in computational audio analysis with a variety of applications. Steelpan audio has proven difficult for general pitch detection methods CRéPE and pYin. CRéPE is a method that uses a deep convolutional neural network to perform pitch estimation directly from the audio signal while pYIN is a digital signal processing-based approach. Audio feature extraction is the process of using digital signal processing techniques to extract low level audio information from signals. Combining audio feature extraction with logistic regression is currently the best performing steelpan pitch estimation method, but the efficacy of using deep neural networks in lieu of traditional machine learning algorithms has yet to be determined. This paper compares the performance and computational requirements of a deep neural network-based architecture against logistic regression as well as the established pYIN and CRéPE pitch detection methods to determine which method is the most accurate and efficient. All of these methods are evaluated on a test dataset containing one-hit audio samples from several distinct sounding steelpans. Generalization to other steelpans is assessed by including samples in the test dataset from steelpans for which no samples are in the training dataset.

RETRACTED ARTICLE: Powerful basic frequency extraction from monophonic signs utilizing versatile sub-band separating

Basic frequency (f0) extraction assumes a significant function in the handling of monophonic signals, for example, discourse and tune. It is fundamental in different ongoing applications like feeling acknowledgment, discourse/performing voice segregation, etc. A few f0 extraction techniques have been proposed throughout the long term, yet nobody calculation functions admirably for both discourse and tune. Previously an efficient method for extracting pitch from speech signals for HMM-based speech synthesis system, voicing detection and pitch estimation is performed using the mean signal obtained from Continuous Wavelet Transform (CWT) coefficients. Both objective and subjective evaluation results show that the quality of speech synthesized with the proposed pitch estimation method is much better compared with HMM-based speech synthesis systems developed using the state-of-the-art pitch extraction methods, namely, Robust Algorithm for Pitch Tracking (RAPT) and Speech Transformation and Representation using Adaptive Interpolation of weighted spectrum (STRAIGHT) employed in HTS. Zero-frequency filter (ZFF) is used to derive the locations of impulse excitation. An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds, the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. In this paper, we propose a novel methodology that can precisely appraise f0 from discourse just as tunes. To begin with, voiced/unvoiced recognition is performed utilizing a novel RNN-LSTM based methodology. At that point, each voiced edge is disintegrated into a few sub-groups. From each sub-band of a voiced casing, the applicant pitch periods are distinguished utilizing autocorrelation and non-straight activities. At last, Viterbi interpreting is utilized to shape the last pitch forms. The presentation of the proposed technique is assessed utilizing mainstream discourse (Keele, CMU-Cold), and melody (MIR-1K, Verses) information bases. The assessment results show that the proposed strategy performs similarly well for discourse and monophonic melodies and is superior to the best in class techniques. Further, the viability of proposed f0 extraction strategy is exhibited by building up an intelligent SARGAM learning apparatus. The most generally utilized f0 extraction strategies are Praat and Riveted. The proposed method increases the performance nearly 81.8% and 35.2% compared to existing methods.

Robust Pitch Estimation and Tracking For Speakers Based on Subband Encoding and The Generalized Labeled Multi-Bernoulli Filter

This paper proposes a new pitch estimator and a novel pitch tracker for speakers. We first decompose the sound signal into subbands using an auditory filterbank, assuming time–frequency sparsity of human speech. Instead of directly selecting the number of subbands according to experience, we propose a novel frequency coverage metric to derive the number of subbands and the center frequencies of the filterbank. The subband signals are then encoded inspired by the computational auditory scene analysis approach, and the normalized autocorrelations are calculated for pitch estimation. To suppress spurious errors and track the speaker identity, the temporal continuity constraint is exploited and a generalized labeled multi-Bernoulli filter is adapted for pitch tracking, where we use a novel pitch state transition model based on the Ornstein–Uhlenbeck process, and the measurement-driven birth model for adaptive new births of pitch targets. Experimental evaluations with various additive noises demonstrate that the proposed methods have achieved better accuracy compared with several state-of-the-art pitch estimation methods in most studied scenarios. Tests using real recordings in a reverberant room also show that the proposed method is robust against reverberation.

Robust Pitch Extraction Method for the HMM-Based Speech Synthesis System

This letter proposes an efficient method for extracting pitch from speech signals for the hidden Markov model (HMM)-based speech synthesis system (HTS). In the proposed method, voicing detection and pitch estimation is performed using the mean signal obtained from continuous wavelet transform coefficients. The proposed pitch extraction method is integrated in the HMM-based speech synthesis system. The Performance of the proposed method is evaluated on CMU Arctic and Keele databases. Both objective and subjective evaluation results show that the quality of speech synthesized with the proposed pitch estimation method is much better compared with HMM-based speech synthesis systems developed using the state-of-the-art pitch extraction methods, namely, robust algorithm for pitch tracking and speech transformation and representation using adaptive interpolation of weighted spectrum employed in the HTS.

Robust Harmonic Features for Classification-Based Pitch Estimation.

Pitch estimation in diverse naturalistic audio streams remains a challenge for speech processing and spoken language technology. In this study, we investigate the use of robust harmonic features for classification-based pitch estimation. The proposed pitch estimation algorithm is composed of two stages: pitch candidate generation and target pitch selection. Based on energy intensity and spectral envelope shape, five types of robust harmonic features are proposed to reflect pitch associated harmonic structure. A neural network is adopted for modeling the relationship between input harmonic features and output pitch salience for each specific pitch candidate. In the test stage, each pitch candidate is assessed with an output salience that indicates the potential as a true pitch value, based on its input feature vector processed through the neural network. Finally, according to the temporal continuity of pitch values, pitch contour tracking is performed using a hidden Markov model (HMM), and the Viterbi algorithm is used for HMM decoding. Experimental results show that the proposed algorithm outperforms several state-of-the-art pitch estimation methods in terms of accuracy in both high and low levels of additive noise.

Pitch estimation based on instantaneous robust algorithm for pitch tracking using weighted linear prediction-based complex speech analysis

Pitch estimation plays an important role on speech processing such as speech coding, synthesis, recognition, and so on. Although current pitch estimation method performs well under clean condition, the performance deteriorates significantly in noisy environment. For this reason, robust pitch estimation against additive noise is required. Modified auto-correlation method is commonly used as pitch estimation in which LP residual is used to compute the auto-correlation. We have previously proposed pitch estimation methods based on Time-Varying Complex AR (TV-CAR) analysis whose criterion is the weighted correlation of the complex residual obtained by the TV-CAR analysis, sum of the harmonics for the complex residual spectrum, or so on. On the other hand, Azarov et al. have proposed an improved method of RAPT (Robust Algorithm for Pitch Tracking) using an instantaneous harmonics that is called IRAPT (Instantaneous RAPT). The IRAPT can perform better estimation than RAPT. Since IRAPT uses band-limited analytic signal to obtain harmonic frequencies, the complex residual signal obtained by the TV-CAR analysis can also be applied to the IRAPT. In this paper, novel pitch estimation method using the instantaneous frequency based on the robust WLP (Weighted Linear Prediction) TV-CAR residual is proposed and evaluated.

Robust Estimation and Tracking of Pitch Period Using an Efficient Bayesian Filter

In this paper, we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters. For this purpose, we propose a general Bayesian model, which is robust to the nonstationary variations of the amplitude and frequency of the input signal. We also employ a state-space model, which uses the delayed versions of the input signal to model the periodicity of nonstationary audio signals. This simple model allows a significant reduction of the required number of particles for the estimation of the pitch period compared to the state-of-the-art particle filtering methods. Moreover, we propose to estimate the logarithm of the period instead of the period itself. We show that the resulting algorithm does not require prior knowledge about the initial state and is robust to the octave error phenomenon, which is a common problem in pitch period estimation methods. Most of the existing methods require that the processing window be longer than the largest existing period of the input signal. In contrast, the proposed method does not impose such a limit. Our method often results in a higher time-domain resolution with no perceptible compromise on the frequency-domain resolution, especially for high-pitched audio signals such as music. Simulation results reveal that the proposed algorithm outperforms the state-of-the-art pitch period detection algorithms at low signal to noise ratios assuming no prior knowledge about the initial conditions.

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterized by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signal, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results.

An adaptive penalty multi-pitch estimator with self-regularization

This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half the true fundamental frequency, the sub-octave, is chosen instead of the true pitch. Extending on current group LASSO-based methods for pitch estimation, this work introduces an adaptive total variation penalty, which enforces both group- and block sparsity, as well as deals with errors due to sub-octaves. Also presented is a scheme for signal adaptive dictionary construction and automatic selection of the regularization parameters. Used together with this scheme, the proposed method is shown to yield accurate pitch estimates when evaluated on synthetic speech data. The method is shown to perform as good as, or better than, current state-of-the-art sparse methods while requiring fewer tuning parameters than these, as well as several conventional pitch estimation methods, even when these are given oracle model orders. When evaluated on a set of ten musical pieces, the method shows promising results for separating multi-pitch signals.

Optimal method for pitch period estimation in scilab

Optimal method for pitch period estimation in scilab

Pitch estimation of speech and music sound based on multi-scale product with auditory feature extraction

The pitch is a crucial parameter in speech and music signals. However, due to severe noisy conditions, missing harmonics, unsuitable physical vibration, the determination of pitch presents a great challenge when desiring to get a good accuracy. In this paper, we propose a method for pitch estimation of speech and music sounds. Our method is based on the fast Fourier transform (FFT) of the multi-scale product (MP) provided by a feature auditory model of the sound signals. The auditory model simulates the spectral behaviour of the cochlea by a gammachirp filter-bank, and the out/middle ear filtering by a low-pass filter. For the two output channels, the FFT function of the MP is computed over frames. The MP is based on constituting the product of the speech and music wavelet transform coefficients at three scales. The experimental results show that our method estimates the pitch with high accuracy. Besides, our proposed method outperforms several other pitch detection algorithms in clean and noisy environments.

Pitch Estimation for CryoSat by Analysis of Stacks of Single-Look Echoes

This letter describes a novel approach to retrieve the pitch of the CryoSat satellite by the analysis of Synthetic Interferometric Radar ALtimeter (SIRAL) acquisitions in synthetic aperture radar (SAR) or SAR-interferometric mode over ocean. The power distribution of the single-look echoes, which are gathered for each sample location on the Earth surface, has been verified to be modulated by the along-track antenna pattern. Thus, the actual antenna pointing direction can be retrieved by fitting the model of the along-track antenna pattern on the power distribution of a stack, which is a collection of single-look echoes referred to the same ground location but acquired from different look angles. With the pitch measurement being independent of the attitude information returned by the onboard star trackers, by comparison of the pitch measured from data and of the pitch from the star tracker, the existence of a bias on the pitch can be verified. Exploiting the proposed approach, the pitch returned by star trackers on CryoSat has been found to be biased by about 0.0533°, confirming similar results obtained by other methods proposed in the literature. It is worth noticing that the method for pitch estimation here described is not limited to CryoSat but can be applied to any SAR altimeter without loss of generality.

Joint estimation of pitch and direction of arrival: improving robustness and accuracy for multi-speaker scenarios

In many speech communication applications, robust localization and tracking of multiple speakers in noisy and reverberant environments are of major importance. Several algorithms to tackle this problem have been proposed in the last decades. In this paper, we propose several extensions to a recently presented joint direction of arrival (DOA) and pitch estimation method, increasing its robustness in multi-speaker scenarios, noise, and reverberation. First, a spectral comb filter is added to the original algorithm to better cope with concurrent speakers. Second, the well-known generalized cross-correlation with phase transform (GCC-PHAT) is used as an additional weighting function to improve the DOA estimation accuracy in terms of correct hits. Third, using multiple microphone pairs, the multi-channel cross-correlation approach is incorporated to improve the robustness against noise and reverberation. In order to improve tracking for moving and even intersecting speakers, a particle filter is used. Experiments with real-world recordings in realistic acoustic conditions show that the proposed extensions increase the DOA hit rate by about 33% compared to the original algorithm for two step-wise moving sources at a signal-to-noise ratio (SNR) of 15 dB and a reverberation time RT60 of 560 ms.

Pitch estimation based on harmonic salience

The comb structure formed by the fundamental frequency and its harmonic partials in the spectrum is the important distinction between the pitch and the white noise or other coloured noises. A pitch estimation method based on harmonic salience is proposed which utilises the characteristic mentioned above to enhance the noise immunity and suppress the half-frequency errors and multi-frequency errors effectively. Experiments on various noised data of the TIMIT database demonstrate the robustness compared with other state-of-the art pitch estimation approaches.

A New Pitch Estimation Method Based on AMDF

In this paper, a new modified average magnitude difference function (MAMDF) is proposed which is robust for noise-corrupt speech pitch estimation. The traditional technology in pitch estimation can easily give rise to the problem of detecting error pitch period. And their estimation performance behaves badly with the occurrence of background noise. In the process of calculation on speech samples, MAMDF presented in this paper has the property of strengthening the characteristic of pitch period and reducing the influence of background noise. And therefore, MAMDF can not only decrease the disadvantage brought by the decreasing tendency of pitch period but also overcome the error caused by severe variation between neighboring samples. The experiment which is implemented in CSTR database shows that MAMDF is greatly superior to AMDF and CAMDF both in clean speech environment and noisy speech environment, representing prominent precision and robustness in pitch estimation.

Harmonic modification and data adaptive filtering based approach to robust pitch estimation

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (PFS). The dominant harmonic (DH) of the PFS is determined and enhanced its amplitude. Normalized autocorrelation function (NACF) is applied to that modified signal. Then empirical mode decomposition (EMD) based data adaptive time domain filtering is applied to the NACF signal. Partial reconstruction is performed in EMD domain. The pitch period is determined from the partially reconstructed signal. The experimental results show that the proposed method performs better than the other recently developed methods for noisy and clean speech signals in terms of gross and fine pitch errors.

Robust query-by-singing/humming system against background noise environments

Under background noise environments, the performance of the Query-by-Singing/Humming (QbSH) system is considerably degraded. Since human pitch information is used as a feature vector for the QbSH system, a noise robust pitch estimation algorithm is inevitable. Thus, a novel pitch-estimation method is proposed by integrating temporal-autocorrelation and spectral-salience methods. As a pre-processing block, spectral smoothing is applied to enhance the stationarity of the noisy input signal. To calculate the similarity between the MIDI database and input humming signal, the dynamic time warping (DTW) algorithm is used. Jang's corpus and AURORA2 database are selected as humming and background noise signals, respectively. Compared with the standard pitch estimation algorithm in the ITU-T G.729 speech codec, the proposed pitch estimation method improves the average accuracy by 11.7% for the 0 dB signal-to-noise ratio (SNR) noise case. It also improves top-20 ratio and mean reciprocal rank (MRR) of the proposed QbSH system, on average, by 7.4% and 0.13, respectively.

A Computationally Efficient Method for Polyphonic Pitch Estimation

This paper presents a computationally efficient method for polyphonic pitch estimation. The method employs the Fast Resonator Time-Frequency Image (RTFI) as the basic time-frequency analysis tool. The approach is composed of two main stages. First, a preliminary pitch estimation is obtained by means of a simple peak-picking procedure in the pitch energy spectrum. Such spectrum is calculated from the original RTFI energy spectrum according to harmonic grouping principles. Then the incorrect estimations are removed according to spectral irregularity and knowledge of the harmonic structures of the music notes played on commonly used music instruments. The new approach is compared with a variety of other frame-based polyphonic pitch estimation methods, and results demonstrate the high performance and computational efficiency of the approach.