Design and implementation of a robust pitch detector based on a parallel processing technique

This dissertation addressed the problem of detecting and estimating the frequency components in a time varying signal. Specifically, we studied the problems of estimating the pitch frequency of a voiced speech signal and extracting the frequency components in a music signal. Our pitch estimation research clarified some relationships among the existing pitch determination algorithms (PDAs). It also resulted in a few new PDAs: Firstly, a multi-channel PDA was proposed for use with speech data recorded using a microphone array. This algorithm jointly estimates the pitch and signal's time delay at each microphone. Secondly, a class of variable frame PDAs was proposed. In addition, we presented a new ML formulation of the pitch estimation problem. This formulation removes the singularity in the previous formulation caused by the number of unknown model parameters increasing with the unknown pitch period. Two joint pitch and spectrum estimators were derived, respectively, for the white and autoregressive spectrum models. Finally, we generalized these two estimators such that they can use multi-channel data to jointly estimate pitch, spectrum and time delay of the signal. As for the problem of estimating the frequency components in a music tone signal, we proposed a phase interpolation algorithm for use in an existing music synthesis algorithm and a new analysis-based music analysis and synthesis algorithm. The proposed phase interpolation algorithm uses quadratic spline functions to model the phase track. With respect to the perceived sound quality, it is much better than the existing algorithm using the quadratic phase and is comparable to the one that uses a cubic phase. However, the latter requires more computation and storage space. The proposed analysis-based music analysis-and-synthesis algorithm aims to improve analysis accuracy and increase synthesis efficiency of the similar algorithms in the literature. The analysis accuracy is improved by using a global waveform fitting approach to estimating the model parameters, in contrast to the frame-based Fourier transform approach used in the existing algorithms. The synthesis efficiency in both computation time and storage space is increased by using a quadratic phase model rather than the cubic phase model used in the previous algorithms.

An accurate silence-unvoiced-voiced classification and pitch detection algorithm is described and its implementation for real-time applications on a Texas Instruments TMS320C25 digital signal processor is evaluated. Speech classification is separated into silence detection and voice-unvoiced classification. Only the signal's energy level and zero-crossing rate are used in both classification processes. Pitch detection need only operate on voiced periods of speech. A peak picking technique is used to successively home in on the peaks that bound the pitch periods. Tests are performed on the found peaks to ensure that they are pitch-period peaks. A real-time implementation strategy is developed that combines silence detection with the signal acquisition and tightly couples voiced-unvoiced classification with pitch detection. The silence detection task is interrupt-driven and the pitch detection task loops continuously. The execution speed and accuracy results for this algorithm are shown to compare favorably with those for other such algorithms published in the literature. >

Video sensor data has been widely used in automatic surveillance applications. In this study, we present a method that automatically detects pigs in a pig room by using depth information obtained from a Kinect sensor. For a real-time implementation, we propose a means of reducing the execution time by applying parallel processing techniques. In general, most parallel processing techniques have been used to parallelize a specific task. In this study, we consider parallelization of an entire system that consists of several tasks. By applying a scheduling strategy to identify a computing device for each task and implementing it with OpenCL, we can reduce the total execution time efficiently. Experimental results reveal that the proposed method can automatically detect pigs using a CPU-GPU hybrid system in real time, regardless of the relative performance between the CPU and GPU.

Speech is the vocalized form of communication. Speech is articulated by moving the tongue, lips, lower jaw to shape the voiced or unvoiced airflow. Emotion recognition results from speech signals, focusing on the extraction of emotion features. The intention of a spoken utterance can be affected by the emotions. In human-computer or human-human interaction systems, emotion recognition systems could provide users with improved services by being adaptive to their emotions. This paper aims to compute the Denominator (AR), Gain (G), Reflection (R) coefficients of All Pole Model and then recognize the emotions from the speech. The important steps followed are Pitch Estimation, Feature Extraction, Statistical Analysis, Classification of the emotions from the speech signal. The speech signal is obtained from the wave file. A single sentence is considered and the Pitch of the sentence can be identified by the Pitch Marker Algorithm. The statistical Classification of the emotions can be done by the Discriminant Analysis and K-NN classifiers to achieve a better success rate. The results show that the negative emotions Anger, Sad, Boredom, Disgust achieved a higher recognition rate than the other emotions. The emotion Sad attained the maximum recognition rate of 100% with the GAIN coefficients under the MDA classification. The database used here is the Berlin Database.

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.

The paper proposes a pitch detection algorithm based on the short-time average magnitude difference function (AMDF) and the short-term autocorrelation function (ACF). At first, AMDF values are computed by AMDF algorithm for a frame of speech signal. And then ACF values are computed by ACF algorithm for the AMDF values. In order to decreases computational expense and complexity, the AMDF values of the frame of speech signal are then transformed into one bit signals. The method can also decrease the effects of amplitude and formants the speech signal for pitch detection. The pitch period is calculated by ACF algorithm for the one bit signals. The multiplication operation for short-time autocorrelation function of the one bit signals is replaced by simple addition operation. The algorithm can largely reduce its computational cost compared with calculating ACF directly. The experiments show that the algorithm can decreases computational expense and complexity compared with ACF algorithm. The algorithm can decrease the error rate of pitch detection compared with AMDF algorithm. The proposed algorithm is easy to implement in real-time signal processing

The pitch of voiced speech sounds provides very important information in speech analysis. Pitch estimation is a difficult task when unprevented noise exists. However experimental results have shown that even robust pitch detection techniques fail in noisy environment with periodic patterns such as noise generated by machines. Wavelet transform, with its special properties in time frequency relation, can be used to detect pitch with remarkable advantage in noise resistance. In wavelet signal analysis, the modulus of the transform have been used extensively, however, we found that the phase information is equally important especially for pitch detection. Since the phase spectrum is always intensive to noise, a more promising pitch period can be obtained from the phase diagram. Properties of the phase pattern in wavelet transform are investigated and the result is applied to construct a robust pitch detector. In our first test, the detector is employed to detect the pitches of a set of speech signals with white noise. We found that our approach clearly outperforms other non-wavelet methods with low signal-to-noise ratio. Sinusoidal noise with different frequency levels is used in the second test. Simulation results have shown that our system works quite stable in such an environment.

This paper presents a new space vector PWM method for a 3-level flying-capacitor inverter. In the proposed technique, boundary restrictions can be easily incorporated to minimize the harmonic distortion of the output voltages. Simple algebraic equations are used to calculate the pulse widths of the gate signals from the sampled reference voltages. This feature make easy the implementation in real time. The voltages on the flying capacitors are regulated by simple ON-OFF controllers independently of the output voltage control. The main features of the proposed technique are analyzed by computer simulations. The experimental tests obtained confirm the expected results.

Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013

Information regarding the pitch period of a speech signal is an important parameter in designing a speech recognition system for tone based languages such as Chinese. Chinese speech has four fundamental tone types: the tone for a given word is mainly characterized by its vowel section. The four tone types are distinct and can be recognized by examining the tone contour. Researchers have developed several different techniques for speech pitch detection. One recent technique is an event-based pitch detection scheme utilizing the dyadic wavelet transform (DyWT). This pitch detector compares maxima across wavelet scales to locate the beginning of each pitch period. We extend this pitch detection technique to a set of Chinese tone utterances using both the cubic spline wavelet and the Daubechies 4 wavelet. We investigate the impact of applying two preprocessing techniques, center clipping and half wave rectification, as well as several pitch decision logic methods. The DyWT performance is compared to an autocorrelation based pitch detector. The results show that the DyWT pitch detector compares favorably with the autocorrelation pitch detector, and has better performance for some cases. A variety of pitch decision logic techniques improve the DyWT pitch detector performance over the original method.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, we present an implementation of adaptive Wiener filtering as a speech enhancement technique for the pitch detection purpose from speech signals affected by noise. The adaptive Wiener filtering is used as a pre-processing stage for speech enhancement, and then a combined technique from Auto-Correlation Function (ACF) and Average Magnitude Difference Function (AMDF) is implemented to get accurate results. The main objective is to improve the process of detecting the fundamental frequency of the speech signal. The adaptive Wiener filter shows a superiority in the proposed pitch detection method as compared to the traditional Wiener filterandspectral subtraction.

A new algorithm for pitch detection of the speech signal is introduced. The technique is based on the discrete wavelet transform to classify the speech signal into voiced and unvoiced segments. The wavelet parameters of the voiced segments in two frequency bands are extracted and crosscorrelation is performed to generate a correlation function. Then, a peak detection is applied to extract the pitch period. The algorithm is highly immunized to noise. A comparison between the ordinary methods and this new one is presented. The pitch contour is varying through the utterance period rather of being consider as constant through the analysis periods as in ordinary methods. The results are accurate for speech of signal to noise ratio equals to 2 dB.

Notice of Violation of IEEE Publication Principles<BR><BR>"Sequential Convex Programming for Collaboration of Connected and Automated Vehicles," <BR>by Xiaoxue Zhang, Jun Ma, Zilong Cheng, Frank L. Lewis, and Tong Heng Lee, <BR>in IEEE Transactions on Intelligent Vehicles, Early Access <BR><BR>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <BR><BR>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <BR><BR>"Networked Model Predictive Control for Vehicle Collision Avoidance" <BR> by Bassam Alrifaee, <BR>in RWTH Aachen University, Dissertation, 04 May 2017 <BR>BR>"Sequential convex programming MPC for dynamic vehicle collision avoidance" <BR>by Bassam Alrifaee, Janis Maczijewski, Dirk Abel, <BR>in the Proceedings of the IEEE Conference on Control Technology and Applications, 2017, pp. 2202-2207 <BR><BR> <br/> This paper investigates the collaboration of multiple connected and automated vehicles (CAVs) in different scenarios. In general, the collaboration of CAVs can be formulated as a nonlinear and nonconvex MPC problem. Most of the existing approaches available for utilization to solve such an optimization problem suffer from the drawback of considerable computational burden, which hinders the practical implementation in real time. This paper proposes the use of SCP, which is a powerful approach to solving the nonlinear and nonconvex MPC problem in real time. To appropriately deploy the methodology, as a first stage, SCP requires linearization and discretization when addressing the nonlinear dynamics of the system model adequately. Based on the linearization and discretization, the original MPC problem can be transformed into a QCQP problem. Besides, SCP also involves convexification to handle the associated nonconvex constraints. Thus, the nonconvex QCQP can be reduced to a quadratic programming (QP) problem that can be solved rather quickly. Therefore, the computational efficiency is suitably improved despite the existence of nonlinear and nonconvex characteristics, whereby the implementation is realized in real time. Furthermore, simulation results in three different scenarios of autonomous driving are presented to validate the effectiveness and efficiency of our proposed approach.

Background/Objectives: The speech signal is an attractive research issue that solves signal noises. For this reason, the paper main goal is to effectively ensure speech quality. An accurate Optimized Speech Enhancement Algorithm (OSEA) has been proposed and implemented to improve speech intelligibility and quality. The proposed approach was developed and implemented through a Discrete Wavelet Transform (DWT) and kalman filter methods. To assess speech quality, various noisy types for each SNR level were used. The obtained results were compared with a wavelet based speech enhancement using objective assessments like SNR, NRMSE and PESQ. Indeed, the obtained results demonstrated a better speech quality .All prepared tests have been implemented on a TMS320C6416 fixed –point digital signal processor which appears to be suitable platform for real-time requirements.

This paper addresses the design of a system for automatic sign interpretation (ASI) with specific applications to road sign imagery for driver assistance. Other applications include intelligent highway systems, and sign inventory systems for transportation departments. This paper extends past work in which a prototype system for the detection of road signs was designed and tested on a limited set of images and sign types. The specific enhancements reported in this paper include the following: (i) enhanced dual-band spectral analysis in the hue-saturation-intensity (HSI) and RGB domains, (ii) the introduction of a relational analysis scheme to form the identification stage, (iii) the creation of a larger database of road sign images encompassing all road sign types encountered in the United States, and (iv) testing under adverse conditions with non-ideal road sign images. The proposed method can be adapted for implementation in real time as this was one of the design considerations. The human cognition of signs under adverse conditions is one of the basic research questions that is yet to be answered. The ASI system designed help to shed some light on the human cognitive process.