Pitch Detection Research Articles

Effect of Listening Biographies on Frequency Following Response Responses of Vocalists, Violinists, and Non-Musicians to Indian Carnatic Music Stimuli.

Background and ObjectivesThe current study investigates pitch coding using frequency following response (FFR) among vocalists, violinists, and non-musicians for Indian Carnatic transition music stimuli and assesses whether their listening biographies strengthen their F0 neural encoding for these stimuli.Subjects and MethodsThree participant groups in the age range of 18-45 years were included in the study. The first group of participants consisted of 20 trained Carnatic vocalists, the second group consisted of 13 trained violinists, and the third group consisted of 22 non-musicians. The stimuli consisted of three Indian Carnatic raga notes (/S-R2-G3/), which was sung by a trained vocalist and played by a trained violinist. For the purposes of this study, the two transitions between the notes T1=/S-R2/ and T2=/R2-G3/ were analyzed, and FFRs were recorded binaurally at 80 dB SPL using neuroscan equipment.ResultsOverall average responses of the participants were generated. To assess the participants’ pitch tracking to the Carnatic music stimuli, stimulus to response correlation (CC), pitch strength (PS), and pitch error (PE) were measured. Results revealed that both the vocalists and violinists had better CC and PS values with lower PE values, as compared to non-musicians, for both vocal and violin T1 and T2 transition stimuli. Between the musician groups, the vocalists were found to perform superiorly to the violinists for both vocal and violin T1 and T2 transition stimuli.ConclusionsListening biographies strengthened F0 neural coding, with respect to the vocalists for vocal stimulus at the brainstem level. The violinists, on the other hand, did not show such preference.

Enhanced Direct Power Control Strategy of a DFIG-Based Wind Energy Conversion System Operating Under Random Conditions

The main objective of this paper is the performances analysis of an Enhanced Direct Power Control (EDPC), applied to Doubly Fed Induction Generator (DFIG) driven by variable speed Wind Turbine (WT). This control strategy uses hysteresis regulators and switching table for active and reactive powers control. These latter are estimated using rotor currents and grid voltages instead of a traditional measurement of stator currents. In addition, the EDPC switching table is based on the position of the rotor flux instead of the stator flux in order to have better regulation accuracy because the rotor voltage vector directly influences the rotor flux and has a proportional relationship with the active and reactive powers. All the operating modes (sub-synchronous, super-synchronous, synchronous and over-speed) of the variable speed WT-DFIG system and the possibility of local reactive power compensation are reported and discussed in this paper. Depending on the operating zone of the WT, Maximum Power Point Tracking (MPPT) technique and pitch angle control are considered to optimize the wind energy efficiency. The validation of the proposed EDPC strategy has been performed through simulation tests under MATALB/Simulink, the obtained results show robustness and good performances with low THD of the generated currents.

Identifying Risk Factors of Upper Extremity Injuries in Collegiate Baseball Players: A Pilot Study.

BackgroundRepetitive pitching places tremendous forces on the shoulder and elbow which can lead to upper extremity (UE) or lower extremity (LE) overuse injuries.PurposeThe purpose of this study was to evaluate pre-season physical measurements in collegiate baseball players and track in-season baseball throwing volume to determine which factors may predict throwing overuse injuries.Study DesignRetrospective Cohort study.MethodsBaseline preseason mobility, strength, endurance, and perception of function were measured in 17 collegiate baseball pitchers. Participants were then followed during the course of the season to collect rate of individual exposure, estimated pitch volume, and rating of perceived exertion in order to determine if changes in workload contributed to risk of injury using an Acute-to-Chronic Workload ratio (ACWR).ResultsParticipants developing an injury had greater shoulder internal rotator strength (p=0.04) and grip strength in a neutral position (p=0.03). A significant relationship was identified between ACWR and UE injuries (p <0.001). Athletes with an ACWR above or below 33% were 8.3 (CI95 1.8-54.1) times more likely to suffer a throwing overuse injury occurring to the upper or lower extremity in the subsequent week.ConclusionACWR change in a positive or negative direction by 33% was the primary predictor of subsequent injury. This finding may assist sports medicine clinicians by using this threshold when tracking pitch volume to ensure a safe progression in workload during a baseball season to reduce the risk of sustaining overuse upper or lower extremity injuries.Level of Evidence3b

SŁUCHOWE POTENCJAŁY WYWOŁANE FFR U WOKALISTÓW, SKRZYPKÓW I NIEMUZYKÓW W ODPOWIEDZI NA BODŹCE W POSTACI MUZYKI KARNATYCKIEJ

BackgroundThe current study investigates pitch coding among vocalists, violinists, and non-musicians to Carnatic musical stimuli.Material and methodsThree groups of participants were included in the study: 10 trained Carnatic vocalists, 10 violinists,and 10 non-musicians. Their ages ranged from 18 to 45 years. Two types of stimuli were given: three notes of a Carnatic raga (S R2 G3) sung by a trained vocalist and three notes of a Carnatic raga (S R2 G3) played on violin by a trained violinist. Frequency following responses (FFRs) were recorded binaurally at 80 dBSPL for both stimuli using Neuroscan equipment.ResultsGrand average responses of all participants were generated. To assess a participant’s pitch tracking to the Carnatic music stimuli, stimulus-to-response correlation, pitch strength, and pitch error were calculated. Vocalists and violinists had better stimulus-to-response correlation and pitch strength values with lower pitch error values than non-musicians for both vocal and violin stimuli. Within both the vocalist and violinist groups, superior performance was noticed for the vocal stimulus compared to the violin stimulus. No such preference was evident among non-musicians.ConclusionsClassical music training dependent plasticity can be demonstrated at brainstem level itself. This holds true for both vocal and violin music, a finding not reported previously. Further a link between musical training and the FFR response can be more strongly demonstrated for vocalists than for violinists.

Rapid Assessment of Non-Verbal Auditory Perception in Normal-Hearing Participants and Cochlear Implant Users.

In the case of hearing loss, cochlear implants (CI) allow for the restoration of hearing. Despite the advantages of CIs for speech perception, CI users still complain about their poor perception of their auditory environment. Aiming to assess non-verbal auditory perception in CI users, we developed five listening tests. These tests measure pitch change detection, pitch direction identification, pitch short-term memory, auditory stream segregation, and emotional prosody recognition, along with perceived intensity ratings. In order to test the potential benefit of visual cues for pitch processing, the three pitch tests included half of the trials with visual indications to perform the task. We tested 10 normal-hearing (NH) participants with material being presented as original and vocoded sounds, and 10 post-lingually deaf CI users. With the vocoded sounds, the NH participants had reduced scores for the detection of small pitch differences, and reduced emotion recognition and streaming abilities compared to the original sounds. Similarly, the CI users had deficits for small differences in the pitch change detection task and emotion recognition, as well as a decreased streaming capacity. Overall, this assessment allows for the rapid detection of specific patterns of non-verbal auditory perception deficits. The current findings also open new perspectives about how to enhance pitch perception capacities using visual cues.

Design ofasoft sensing technique for measuring pitch and yaw angular positions foraTwinRotor MIMOSystem.

This paper presentsa soft sensor design technique for estimation of pitch and yaw angular positions of a Twin Rotor MIMO System (TRMS).The objective of the proposed workwas to calculate the value of pitch and yaw angular positions using a stochastic estimation technique. Methods:Measurements from optical sensorswereused to measure fan blade rotationsper minute(RPM). The Kalman filter,which is a stochastic estimator,was used in the proposed system and its resultswere compared with those oftheLuenbergerobserver and neural network. The TwinRotor MIMOSystem is a nonlinear system with significant cross coupling between its rotors. Results:The estimatorswere designed for the decoupled system andwere applied in real life to the coupled TRMS. The convergence of estimation to the actual valueswas checked on a practical setup. The Kalman filter estimatorswere evaluated for various inputs and disturbances, and the resultswere corroborated in real time. Conclusion:From the proposed work itwas seen thattheKalman filterhadatleastIntegral Absolute Error(IAE),Integral Square Error(ISE),Integral Time Absolute Error(ITAE)as compared totheneural network andtheLuenbergerbased observer.

Improved steelpan pitch detection through audio feature extraction and machine learning

The steelpan, a significant musical instrument invented in the mid-20th century, is a harmonically complex system making fundamental pitch detection a difficult task. Initial experiments using state-of-the-art pitch detection algorithms have shown significant difficulties in detecting the pitch of individual steelpan notes. This paper presents a method of improved pitch detection accuracy by combining music information retrieval techniques with machine learning algorithms. An audio sample set consisting of thousands of steelpan notes from ten different tenor steelpans is used to quantitatively evaluate the proposed methodology. Low-level audio features are extracted from the audio samples and used to train a machine learning algorithm which identifies the salient features for pitch estimation. This method’s performance is measured against the current state-of-the-art pitch detection methods, pYIN (a probabilistic autocorrelation method) and CRéPE (a convolutional neural network-based method), showing improved performance at steelpan pitch detection. The machine learning model developed for this paper using this methodology is focused on the tenor steelpan but is intended to lead to a more generalized model for pitch detection of all types of steelpans.

Novel model for pitch estimation using hybrid DWT-DCT HPS

Pitch is an important feature of speech. Therefore, extraction of pitch becomes a vital task for processes like speaker coding, speaker recognition, speech synthesis, speech recognition and many such applications. The few available algorithms such as Discrete Cosine Transform (DCT) based pitch extraction, harmonic product spectrum (HPS) which is obtained from DCT are useful for extraction of pitch. In this paper, we propose a hybrid Discrete Wavelet Transform-Discrete Cosine Transform (DWT- DCT HPS) based pitch extraction. A voice sample is taken and de-segmented into 36 bands in the frequency domain. Then on those bands spatial domain transformation is performed to get the most prominent features. The Gross pitch error (GPE) and Fine pitch error (FPE) criteria is used as a measure to find the accuracy of the novel method. The result depicts that the novel proposed Hybrid method is better as compared to DCT-HPS in terms of Pitch error.

Jazz Bass Transcription Using a U-Net Architecture

In this paper, we adapt a recently proposed U-net deep neural network architecture from melody to bass transcription. We investigate pitch shifting and random equalization as data augmentation techniques. In a parameter importance study, we study the influence of the skip connection strategy between the encoder and decoder layers, the data augmentation strategy, as well as of the overall model capacity on the system’s performance. Using a training set that covers various music genres and a validation set that includes jazz ensemble recordings, we obtain the best transcription performance for a downscaled version of the reference algorithm combined with skip connections that transfer intermediate activations between the encoder and decoder. The U-net based method outperforms previous knowledge-driven and data-driven bass transcription algorithms by around five percentage points in overall accuracy. In addition to a pitch estimation improvement, the voicing estimation performance is clearly enhanced.

Sensorless real-time reduced order model-based adaptive maximum power tracking pitch controller for grid connected wind turbines

To overcome the mechanical stress and damage caused by the high wind speed and grid transient on the wind turbine (WT) and to ensure the smooth operation of WT, a sensorless maximum power tracking (MPT) pitch controller for grid integrated WT is proposed in this paper. The advantages of the proposed controller are: a) reduced rotor speed oscillations, b) reduced transients in Point of Common Coupling (PCC) voltage, and c) controlled output power of WT without exceeding its thermal limits. The controller is validated on the WT integrated into the frequency-dependent reduced order model (FDROM) of the power grid. Another advantage is that, as the reference speed for the controller is estimated using FDROM, actual wind speed measurements are not required in this approach. To ensure smooth operation, and to limit the aerodynamic power and speed within the normal operating range, the difference between the actual and reference speed of the generator is used as the input to the controller. The architecture is validated using a real-time digital simulator (RTDS) on WT integrated two-area and IEEE-39 bus power system models. The simulation results show that the proposed controller is more effective than the conventional methods during changing system operating conditions.

A Double-EKF Orientation Estimator Decoupling Magnetometer Effects on Pitch and Roll Angles

Accurate orientation estimation using low-cost inertial and magnetic sensors is important. However, since the magnetometer measurements affect pitch and roll estimates in addition to yaw determination, the resultant accuracy reduction under magnetic disturbances is adverse for special occasions where high-precision pitch and roll estimates are essential. This article presents a double-extended Kalman filter (DEKF) orientation estimator with unit quaternion as output, which decouples magnetometer effects on pitch and roll estimates by decomposing the orientation into tilt quaternion and heading quaternion. The tilt quaternion, including pitch and roll information, is estimated by the first EKF where accelerometer and gyroscope measurements are processed and the inaccurate heading information is discarded. The heading quaternion, solely describing the yaw orientation, is determined by the second EKF, which fuses the vertical component of angular rate measurements and the horizontal component of magnetic measurements. True quaternion is denoted by the multiplicative of tilt and heading quaternions. The separate property of DEKF limits the influences of magnetic disturbances on yaw estimation, making the pitch and roll angles immune to them. Experiment results show that the proposed method solves the coupled disturbance problem without losing accuracy.

Compact differential plane interferometer with in-axis mirror tilt detection

Besides the environmental fluctuations, the typical sources of significant uncertainty in the laser interferometry systems are the geometrical errors. These are stemming, among others, from the misalignment of measurement axes, the thermo-mechanical influences of the system components and the mounting, guidance errors of the translation mechanism that carries the measurement mirror or vibrations. We report on a compact double-pass differential plane interferometer that features an original optical arrangement with four parallel and coplanar beams, where the beam pairs in the two arms are coaxial. The differential arrangement minimizes the dead path and shortens the metrological loop so that the sensitivity to thermal drifts and vibrations is reduced. The arm symmetry allows for the preservation of the Abbe principle, and the common path mitigates the influences of the environmental disturbances. The interferometer optics is designed as a self-contained single-piece assembly made using optical contacting from the low-expansion materials. The interferometer system integrates the homodyne receiver (even though the optical arrangement is well-suited for heterodyne detection too) and also a tilt-detection electronics that allows for detection of pitch and roll of the interferometer mirror so that the parasitic movement of the measuring mirror could be compensated for. The experimental characterization revealed a good optical performance of the interferometer with sub-nanometre cyclic error and the resolution of tilt detection in order of a few microradians.

Estimation of Wave Period from Pitch and Roll of a Lidar Buoy.

This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.

Polytonia

This paper first proposes a labeling scheme for tonal aspects of speech and then describes an automatic annotation system using this transcription. This fine-grained transcription provides labels indicating pitch level and pitch movement of individual syllables. Of the five pitch levels, three (low, mid, high) are defined on the basis of pitch changes in the local context and two (bottom, top) are defined relative to the boundaries of the speaker’s global pitch range. For pitch movements, both simple and compound, the transcription indicates direction (rise, fall, level) and size, using size categories (pitch intervals) adjusted relative to the speaker’s pitch range. The automatic tonal annotation system combines several processing steps: segmentation into syllable peaks, pause detection, pitch stylization, pitch range estimation, classification of the intra-syllabic pitch contour, and pitch level assignment. It uses a dedicated and rule-based procedure, which unlike commonly used supervised learning techniques does not require a labeled corpus for training the model. The paper also includes a preliminary evaluation of the annotation system, for a reference corpus of nearly 14 minutes of spontaneous speech in French and Dutch, in order to quantify the annotation errors. The results, expressed in terms of standard measures of precision, recall, accuracy and Fmeasure are encouraging. For pitch levels low, mid and high an F-measure between 0.946 and 0.815 is obtained and for pitch movements a value between 0.708 and 1. Provided additional modules for the detection of prominence and prosodic boundaries, the resulting annotation may serve as an input for a phonological annotation.

Comparative study of pitch detection algorithm to detect traditional Balinese music tones with various raw materials

The characteristics of a tone are formed from the fundamental frequency values that make up the tone. A musical instrument’s tone has different fundamental values in building the timbre of the instrument’s tone. This study compares four PDA (Pitch Detection Algorithm) methods and tries to find the best algorithm for detecting Balinese instruments’ tone with different raw materials, namely leather, metal, and bamboo. The PDA method analyzed was the Autocorrelation, ZCR, HPS, and FFT methods. This study also explores the performance of each PDA (Pitch Detection Algorithm) approach in checking the pitch of traditional Balinese musical instruments and focuses on which of the four PDA (Pitch Detection Algorithm) methods detect Balinese musical instruments by comparing the results of the pitch analysis of each line with the original frequency. This study used 193 tone data consisting of Kendang to represent skin, Gangsa instruments to represent metal, Rindik, and Balinese flutes to represent bamboo material. Based on the test results, the PDA (Pitch Detection Algorithm) method that produces the closest value of the four musical instruments is HPS (Harmonic Product Spectrum) and FFT (Fast Fourier Transform) with an average difference of 4.81 Hz and 4.85 Hz.

Pitch Estimation by Multiple Octave Decoders

Pitch estimation is an essential task in audio processing due to its key role in many speech and music applications. Still, accurately predicting a continuous value from a high range of pitch frequencies is a challenging task. Inspired by the success of signal processing filterbank methods, we propose a novel deep architecture for accurate pitch estimation. The proposed method is composed of an encoder and multiple decoders. The encoder is implemented by a convolutional neural network that provides a good representation of the raw audio signal, and its output is fed into a set of decoders. Each decoder predicts the pitch value within a specific frequency band and is implemented by a fully-connected neural network. Such a construction allows each decoder to specialize in a particular frequency regime, which turns into a more accurate estimation of pitch values for music and speech signals.

Robust Attitude Estimation Using IMU-Only Measurements

This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the IMU-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain robustness. The proposed algorithms provide an adaptive method for tuning the measurement noise covariance such that they can accurately estimate the attitude in the two axes. The first method relies on a single tuning factor, whereas the second one tunes the covariance with different (multiple) factors for each measurement axis. The proposed methodologies are tested and compared with other existing filtering algorithms in the literature under different dynamical conditions and using real-world experimental datasets in order to validate their effectiveness. Results show that highly dynamic scenarios, especially the multiple tuning factor strategy, can increase the attitude estimation accuracy more than two-times compared to the competitive algorithms.

Optimization of Intelligent Tuning System for Stringed Instruments Based on Wireless Sensor Network

With the increasing abundance of material civilization, people’s pursuit of spiritual civilization is also higher and higher. Although tuning is the analysis and processing of sound signal, the string signal is also a kind of sound signal, which follows the basic law of acoustics. In the field of wireless sensor network, this paper introduces the status quo of tuning tools for stringed instruments, analyses the existing problems of tuning tools for mainstream stringed instruments, and puts forward an automatic tuning system based on wireless sensor network. The hardware design of intelligent tuning system based on wireless sensor network includes audio acquisition module, tuning control module driver circuit design and voice broadcast module, and network communication module selection analysis. The software design of intelligent tuning system based on wireless sensor network includes embedded software design and mobile software design. Embedded software design mainly includes system flow design and automatic tuning function design. In this paper, an improved endpoint detection algorithm is used to separate the chord tone from the nonchord tone, and an improved pitch detection algorithm is used to get the accurate pitch frequency of the chord tone signal. The function and performance of the tuning system are tested to verify the reliability of the optimized intelligent tuning system.

Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking

Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of modalities. In this work, we advance self-supervised learning from permutations, by pre-training a model to reorder shuffled parts of the spectrogram of an audio signal, to improve downstream classification performance. We make two main contributions. First, we overcome the main challenges of integrating permutation inversions into an end-to-end training scheme, using recent advances in differentiable ranking. This was heretofore sidestepped by casting the reordering task as classification, fundamentally reducing the space of permutations that can be exploited. Our experiments validate that learning from all possible permutations improves the quality of the pre-trained representations over using a limited, fixed set. Second, we show that inverting permutations is a meaningful pretext task for learning audio representations in an unsupervised fashion. In particular, we improve instrument classification and pitch estimation of musical notes by reordering spectrogram patches in the time-frequency space.

Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cell’s p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events.