Digital processing of speech signals

Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles.

http://bbcr.uwaterloo.ca/~xshen/paper/2015/cvsac.pdf

Connected Vehicles: Solutions and Challenges

Linear Regression Analysis

International Joint Conference on Neural Networks

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

A Survey of Audio-Based Music Classification and Annotation

In this paper, an improved audio classification and categorization technique is presented. This technique makes use of wavelets and support vector machines (SVMs) to accurately classify and categorize audio data. When a query audio is given, wavelets are first applied to extract acoustical features such as subband power and pitch information. Then, the proposed method uses a bottom-up SVM over these acoustical features and additional parameters, such as frequency cepstral coefficients, to accomplish audio classification and categorization. A public audio database (Muscle Fish), which consists of 410 sounds in 16 classes, is used to evaluate the performances of the proposed method against other similar schemes. Experimental results show that the classification errors are reduced from 16 (8.1%) to six (3.0%), and the categorization accuracy of a given audio sound can achieve 100% in the Top 2 matches.

Audio classification and categorization based on wavelets and support vector Machine

It has been shown in the literature that the perceptual wavelet packet decomposition (PWPD) and the Teager energy operator (TEO) are useful for various speech processing systems and speech enhancement applications, respectively. By the use of the PWPD and the TEO, this paper presents an improved wavelet-based speech enhancement method. The main advantage of the proposed method is that the over thresholding of speech segments which is usually occurred in conventional wavelet-based speech enhancement schemes can be avoided. As a consequence, the enhanced speech quality of the proposed method can be increased substantially from those of conventional approaches. In addition, the proposed method does not require a complicated estimation of the noise level or any knowledge of the SNR. Using speech signals corrupted by additive and real noises, experimental results demonstrate that the speech enhancement method presented in this paper is capable of outperforming conventional noise cancellation schemes.

Speech Enhancement Using Perceptual Wavelet Packet Decomposition and Teager Energy Operator

In this paper, an algebraic decoding method is proposed for the quadratic residue codes that utilize the Berlekamp-Massey algorithm. By a modification of the technique developed by He et al., one can express the unknown syndromes as functions of the known syndromes. The unknown syndromes are determined by an efficient algorithm also developed in this paper. With the appearance of unknown syndromes, one obtains the consecutive syndromes that are needed for the application of the Berlekamp-Massey algorithm. The decoding scheme, developed here, is easier to implement than the previous decoding algorithm developed for the Golay code and the (47, 24, 11) QR code. Moreover, it can be extended to decode all codes of the family of binary quadratic residue codes with irreducible generating polynomials.

Algebraic Decoding of Quadratic Residue Codes Using Berlekamp-Massey Algorithm *

This paper presents a remote on-line diagnostic system for vehicles via the use of On-Board Diagnostic (OBD), GPS, and 3G techniques. The main parts of the proposed system are on-board computer, vehicle monitor server, and vehicle status browser. First, the on-board computer can obtain the location of deriver and vehicle status from GPS receiver and OBD interface, respectively. Then on-board computer will connect with the vehicle monitor server through 3G network to transmit the real time vehicle system status. Finally, vehicle status browser could show the remote vehicle status including vehicle speed, engine rpm, battery voltage, engine coolant temperature, and diagnostic trouble codes. According to the experimental results, the proposed system can help fleet managers and car knockers to understand the remote vehicle status. Therefore this system can decrease the time of fleet management and vehicle repair due to the fleet managers and car knockers who find the diagnostic trouble messages in time. Keywords—Diagnostic Trouble Code (DTC), Electronic Control Unit (ECU), Global Position System (GPS), On-Board Diagnostic (OBD).

A Study on Remote On-Line Diagnostic System for Vehicles by Integrating the Technology of OBD, GPS, and 3G

This paper implemented a cloud computation based second generation on-board diagnostic (OBD-II) system. The proposed system is integrated with OBD-II, 3.5G wireless network, and cloud computing technologies. It can perform real-time vehicle status surveillance. The monitored features cover engine rpm, vehicle speed, coolant temperature, fault codes, and other vehicle dynamics information. The vehicle information will be transmitted to the cloud computing server via 3.5G wireless network for fault analysis. Once cloud computing server detects fault conditions, the proposed system could classify the fault conditions depended on vehicle type and its model year. Then the cloud computing server will report the fault code analysis results to the user and provide the description about repair procedure. The proposed system will greatly shorten the time to detect vehicle trouble condition. The system presented in this thesis has a very high value in the applications of vehicle maintenance and fleet management.

Shi-Huang Chen

Papers

Audio classification and categorization based on wavelets and support vector Machine

Speech Enhancement Using Perceptual Wavelet Packet Decomposition and Teager Energy Operator

Algebraic Decoding of Quadratic Residue Codes Using Berlekamp-Massey Algorithm *

A Study on Remote On-Line Diagnostic System for Vehicles by Integrating the Technology of OBD, GPS, and 3G

The Implementation of OBD-II Vehicle Diagnosis System Integrated with Cloud Computation Technology