Fundamental Frequency Estimation Research Articles

Validation of an Algorithm for Semi-automated Estimation of Voice Relative Fundamental Frequency.

Relative fundamental frequency (RFF) has shown promise as an acoustic measure of voice, but the subjective and time-consuming nature of its manual estimation has made clinical translation infeasible. Here, a faster, more objective algorithm for RFF estimation is evaluated in a large and diverse sample of individuals with and without voice disorders. Acoustic recordings were collected from 154 individuals with voice disorders and 36 age- and sex-matched controls with typical voices. These recordings were split into training and 2 testing sets. Using an algorithm tuned to the training set, semi-automated RFF estimates in the testing sets were compared to manual RFF estimates derived from 3 trained technicians. The semi-automated RFF estimations were highly correlated ( r = 0.82-0.91) with the manual RFF estimates. Fast and more objective estimation of RFF makes large-scale RFF analysis feasible. This algorithm allows for future work to optimize RFF measures and expand their potential for clinical voice assessment.

Optimal base frequency estimation of an electrical signal based on Prony’s estimator and a FIR filter

In the frequency domain modeling of power supplying networks, it is usually necessary to determine the frequency spectrum of the currents flowing through the nonlinear devices (Arrillaga and Watson, 2003; Das, 2015; Lewandowski and Walczak, 2014). The spectrum estimation can be done in many different ways (Lewandowski and Walczak, 2014; Łobos and Rezmer, 1997; Ray et al., 2016). In work (Lewandowski and Walczak, 2014) a spectrum estimation method has been proposed, which has better accuracy in comparison with other competitive methods like WIFTA (Window Interpolated Fourier Transform) or TDQS (Time Domain Quasi-Synchronous Sampling), while maintaining a low demand for computing power. The accuracy of this method depends on two main factors: the accuracy of the fundamental frequency estimation and the accuracy of the signal interpolation in the resampling process. For the estimation of the fundamental frequency, the method uses a first order Prony’s estimator (Łobos and Rezmer, 1997) and a band-pass FIR (Finite Impulse Response) filter.In the paper, an attempt was made to find the optimal parameters for both: the Prony’s estimator and the FIR filter. First, an analysis of the measurement window size of the Prony’s estimator and the number of the FIR filter coefficients was performed. Then, the optimization problem was defined, which is not trivial, since the Prony’s estimator is highly nonlinear and some of the parameters (the window size and the number of filter coefficients) are integers. The research allowed to determine the optimal values of the considered parameters of the frequency estimation method. Along with the results presented in Lewandowski and Walczak (2015) it is now possible to implement the spectrum estimation method from Lewandowski and Walczak (2014) using the optimal set of parameters.

Relating ambulatory voice measures with self-ratings of vocal fatigue in individuals with phonotraumatic vocal hyperfunction

Advancements in mobile and wearable technologies continue to enhance ambulatory voice monitoring for the improved assessment and treatment of behaviorally based voice disorders. Phonotraumatic vocal hyperfunction is one common behaviorally based voice disorder associated with faulty patterns of chronic vocal behavior that result in vocal fold tissue trauma, such as nodules or polyps. As a result, individuals often exhibit dysphonia and elevated levels of vocal fatigue. This study investigated the relationships between self-ratings of vocal fatigue and ambulatory voice measures in 44 patients with vocal fold nodules or polyps and a control group of individuals with normal voices matched for sex, age, and occupation. Using a smartphone-based ambulatory voice monitor, self-ratings were provided on a visual analog scale at five-hour intervals during the day, and data were continuously recorded from a subglottal neck-surface accelerometer. Voice dosimetry metrics and summary statistics of ambulatory voice measures were derived from accelerometer-based estimates of sound pressure level, fundamental frequency, and spectral and cepstral properties. Given the variance inherent in perceptual judgments, the analyses focused on comparisons between time periods that exhibited differences in self-ratings that were determined to be significant (>19.7 points on a 100-point scale).

A Demodulation-Based Technique for Robust Estimation of Single-Phase Grid Voltage Fundamental Parameters

This paper proposes a robust technique for the single-phase grid voltage fundamental amplitude, frequency, and phase angle estimation under distorted grid conditions. It is based on a demodulation method tuned at a fixed frequency. It does not have stability issue due to an open-loop structure, does not require real-time evaluation of trigonometric and inverse trigonometric functions, and also avoids the use of look-up table. It can provide accurate estimation of the single-phase grid voltage fundamental parameters under dc offset and harmonics. When compared with a frequency adaptive demodulation technique, the proposed one is less affected by dc offset, can provide faster frequency estimation, and also avoids interdependent loop, trigonometric, and inverse trigonometric functions operation. Simulation and experimental results are presented to verify the performance of the proposed technique.

Robust Real-Time Music Transcription with a Compositional Hierarchical Model.

The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model’s structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model’s performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.

Three-Parametric Cubic Interpolation for Estimating the Fundamental Frequency of the Speech Signal

In this paper, we propose a three-parametric convolution kernel which is based on the one-parameter Keys kernel. The first part of the paper describes the structure of the three-parameter convolution kernel. Then, a certain analytical expression for finding the position of the maximum of the reconstructed function is given. The second part presents an algorithm for estimating the fundamental frequency of the speech signal processing in the frequency domain using Picking Picks methods and parametric cubic convolution. Furthermore, the results of experiments give the estimated fundamental frequency of speech and sinusoidal signals in order to select the optimal values of the parameters of the proposed convolution kernel. The results of the fundamental frequency estimation according to the mean square error are given by tables and graphics. Consequently, it is used as a basis for a comparative analysis. The analysis derived the optimal parameters of the kernel and the window function that generates the least MSE. Results showed a higher efficiency in comparison to two or three-parameter convolution kernel.

A Study of Peak Finding Algorithms for the Autocorrelation Function of Speech Signal

In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed. According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics - such as monotonic increasing function - of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing. The speech data, is composed of Korean emotion expressed words, is used for evaluation of their performance. The pitches are measured to compare the performance of proposed algorithms.

Accurate Estimation of Single-Phase Grid Voltage Fundamental Amplitude and Frequency by Using a Frequency Adaptive Linear Kalman Filter

This paper reports the performance of a linear Kalman filter (LKF)-based technique to estimate the single-phase grid voltage fundamental amplitude and frequency under distorted grid conditions. An enhanced frequency-locked loop is combined with the LKF to obtain the frequency adaptive capability of the technique. A frequency domain analysis is presented to show the effect of tuning the parameters of the LKF. When compared with a non-LKF, such as the extended KF, the technique is linear and computationally efficient, as it does not contain derivative operations and nonlinear terms. The technique is less affected by dc offset and harmonics, and also shows improved convergence property as compared with an extended complex KF. Simulation and experimental results are provided to validate the performance of the technique.

Singing Voice Separation and Vocal F0 Estimation Based on Mutual Combination of Robust Principal Component Analysis and Subharmonic Summation

This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency (F0) estimation. Vocal F0 estimation is considered to become easier if singing voices can be separated from a music audio signal, and vocal F0 contours are useful for singing voice separation. This calls for an approach that improves the performance of each of these tasks by using the results of the other. The proposed method first performs robust principal component analysis (RPCA) for roughly extracting singing voices from a target music audio signal. The F0 contour of the main melody is then estimated from the separated singing voices by finding the optimal temporal path over an F0 saliency spectrogram. Finally, the singing voices are separated again more accurately by combining a conventional time-frequency mask given by RPCA with another mask that passes only the harmonic structures of the estimated F0s. Experimental results showed that the proposed method significantly improved the performances of both singing voice separation and vocal F0 estimation. The proposed method also outperformed all the other methods of singing voice separation submitted to an international music analysis competition called MIREX 2014.

Fast fundamental frequency determination via adaptive autocorrelation

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.

Harmonic sum-based method for heart rate estimation using PPG signals affected with motion artifacts

Wearable photoplethysmography has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the HSUM model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint HSUM model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset.

Fundamental Frequency Estimation in Speech Signals With Variable Rate Particle Filters

Fundamental frequency estimation, known as pitch estimation in speech signals is of interest both to the research community and to industry. Meanwhile, the particle filter is known to be a powerful Bayesian inference method to track dynamic parameters in nonlinear state-space models. In this paper, we propose a speech model under a time-varying source-filter speech model, and use variable rate particle filters (VRPF) to develop methods for estimation of pitch periods in speech signals. A Rao--Blackwellised variable rate particle filter (RBVRPF) is also implemented. The proposed VRPF and RBVRPF are compared with a state-of-the-art pitch estimation algorithm, the YIN algorithm. Simulation results show that more accurate estimation of pitch can be obtained by VRPF and RBVRPF even under strong background noise conditions.

Vocal dosimeter devices and their uncertainty

Vocal dosimeters may be used to characterize long-term voice production in typical daily activities. The reliability of four vocal dosimeter devices in the estimation of Sound Pressure Level (SPL) and fundamental frequency (Fo) was assessed. The devices evaluated were the Ambulatory Phonation Monitor (KayPENTAX), VoxLog (Sonvox), VocaLog (Griffin Laboratories), and Voice-Care (PR.O.VOICE). Twenty subjects were recorded in a sound booth by means of a head-mounted microphone while using each dosimeter. Subjects were asked to produce a sustained /a/ vowel and to read a text in three different voice styles (relaxed, normal, and raised). On the basis of the difference in the estimation of the parameters between the values acquired from the HMM and the dosimeters, the mean error, and the combined uncertainty were calculated. For the SPL, the dosimeter with the highest mean error was the APM, followed by the VoxLog, the VocaLog, and the Voice-Care. For Fo, the dosimeter with the highest mean error was the VoxLog, followed by the Voice-Care, and the APM (the VocaLog does not monitor Fo).

A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.

A New Biologically Inspired Fuzzy Expert System-Based Voiced/Unvoiced Decision Algorithm for Speech Enhancement

In this paper, we propose a speech enhancement approach for a single-microphone system. The main idea is to apply a specific transformation on the speech signal depending on the voicing state of the signal. We apply a voiced/unvoiced algorithm based on the multi-scale product analysis with the use of fuzzy logic to make more cognitively inspired use of speech information. A comb filtering is applied on the voiced frames of the noisy speech signal, and a spectral subtraction is operated on the unvoiced frames of the same signal. Further, the harmonics are enhanced by performing a designed comb filtering using an adjustable bandwidth. The comb filter is tuned by an accurate fundamental frequency estimation method. The fundamental frequency estimation method is based on computing the multi-scale product analysis of the noisy speech. Experimental results show that the proposed approach is capable of reducing noise in adverse noise environments with little speech degradation and outperforms several competitive methods.

Pitch extraction and fundamental frequency estimation from indian folk music using time domain and frequency domain technique

Pitch extraction and fundamental frequency estimation from indian folk music using time domain and frequency domain technique

Multichannel Double-Talk Detector based on Fundamental Frequency Estimation

Multichannel acoustic echo cancellers aim at reducing the undesired acoustic coupling among microphones and loudspeakers in multichannel audio/video teleconferencing systems. In this scenario, double-talk refers to the situation in which there is an active speaker both in the remote room and in the local room. The detection of double-talk is one of the main issues to be faced in such systems to ensure a correct echo cancellation. Indeed, filters coefficients update has to be suppressed during double-talk in order to prevent the filter from diverging because of the presence of the local speaker. In this paper, a novel approach for double-talk detection is discussed based on the estimation of the fundamental frequency of the residual error signals through adaptive second-order notch filters. More specifically, simultaneous speaker activity in the remote and local rooms is revealed by exploiting the same frequency tracking introduced for multichannel decorrelation. Several experiments have been carried out to prove the effectiveness of the proposed approach also making comparison with other solutions of the state of the art.

An Analysis of Modified Demodulation-Based Grid Voltage Parameter Estimator

The estimation of the grid voltage fundamental parameters, i.e., the phase, frequency, and amplitude, is required for a wide variety of applications, particularly for synchronization and control of grid-connected converters and for monitoring and protection purposes in power systems. To accomplish this task, many approaches have been proposed in the literature. Recently, a modified demodulation-based technique (MDT) has been presented, which aims to accurately extract the grid voltage fundamental parameters under harmonically distorted and frequency-varying conditions. In this letter, it is shown that the MDT is actually a phase-locked loop. The MDT small-signal modeling and stability analysis are then conducted, and some modifications to enhance its performance are suggested. The effectiveness of these modifications is finally confirmed using numerical results.

Single-Phase Grid Voltage Frequency Estimation Using Teager Energy Operator-Based Technique

This paper reports the performance of a technique for the estimation of single-phase grid voltage fundamental frequency under distorted grid conditions. The technique combines a Teager energy operator (TEO) with a frequency adaptive bandpass filter (BPF). The TEO is based on three consecutive samples and is used to estimate the fundamental frequency. The BPF relies on a recursive discrete Fourier transform (RDFT) and an inverse RDFT, and is used to extract the normalized amplitude of the grid voltage fundamental component. The technique is computationally efficient and can also reject the negative effects caused by dc offset and harmonics. It requires less computational effort, can provide faster estimation, and is also less affected by harmonics as compared with a technique relying on the RDFT-based decomposition of the single-phase system into orthogonal components. The performance of the technique is verified using both simulation and experimental results.

High-resolution Spectral-analysis for Fundamental Frequency Estimation of High-rise Buildings subjected to Earthquakes

Accurate estimation of the fundamental frequency of civil structures is crucial for many applications; in particular, this estimation becomes more important when it helps to avoid or minimize human, economic, and structural damages as the case of the design and analysis of high-rise buildings subjected to earthquakes. Although many techniques have been proposed for this task, several aspects such as accuracy, noise immunity, and suitable time-frequency resolution have not been adequately addressed. In this work, the short-time multiple signal classification (ST-MUSIC), a high-resolution spectral-analysis technique, is proposed to estimate the fundamental frequency of high-rise buildings using vibration signals. The proposal is validated and tested using synthetic signals and real measurements. In the latter, the fundamental frequency of a high-rise building, 1:20 scale model, is estimated before-, during-, and after-seismic excitation through the analysis of the generated vibrations. For comparison purposes, the short-time Fourier transform (STFT), a conventional time-frequency technique, is also used. The obtained results show a high accuracy and noise immunity in the analysis, which makes the proposal a suitable and reliable tool for this task.