A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds
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A Thesis Entitled Nocturnal Bird Call Recognition System for Wind Farm
A Thesis entitled Nocturnal Bird Call Recognition System for Wind Farm Applications by Selin A. Bastas Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Electrical Engineering _______________________________________ Dr. Mohsin M. Jamali, Committee Chair _______________________________________ Dr. Junghwan Kim, Committee Member _______________________________________ Dr. Sonmez Sahutoglu, Committee Member _______________________________________ Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo December 2011 Copyright 2011, Selin A. Bastas. This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Nocturnal Bird Call Recognition System for Wind Farm Applications by Selin A. Bastas Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Electrical Engineering The University of Toledo December 2011 Interaction of birds with wind turbines has become an important public policy issue. Acoustic monitoring of birds in the vicinity of wind turbines can address this important public policy issue. The identification of nocturnal bird flight calls is also important for various applications such as ornithological studies and acoustic monitoring to prevent the negative effects of wind farms, human made structures and devices on birds. Wind turbines may have negative impact on bird population. Therefore, the development of an acoustic monitoring system is critical for the study of bird behavior. This work can be employed by wildlife biologist for developing mitigation techniques for both on- shore/off-shore wind farm applications and to address bird strike issues at airports. -
Relating Optical Speech to Speech Acoustics and Visual Speech Perception
UNIVERSITY OF CALIFORNIA Los Angeles Relating Optical Speech to Speech Acoustics and Visual Speech Perception A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical Engineering by Jintao Jiang 2003 i © Copyright by Jintao Jiang 2003 ii The dissertation of Jintao Jiang is approved. Kung Yao Lieven Vandenberghe Patricia A. Keating Lynne E. Bernstein Abeer Alwan, Committee Chair University of California, Los Angeles 2003 ii Table of Contents Chapter 1. Introduction ............................................................................................... 1 1.1. Audio-Visual Speech Processing ............................................................................ 1 1.2. How to Examine the Relationship between Data Sets ............................................ 4 1.3. The Relationship between Articulatory Movements and Speech Acoustics........... 5 1.4. The Relationship between Visual Speech Perception and Physical Measures ....... 9 1.5. Outline of This Dissertation .................................................................................. 15 Chapter 2. Data Collection and Pre-Processing ...................................................... 17 2.1. Introduction ........................................................................................................... 17 2.2. Background ........................................................................................................... 18 2.3. Recording a Database for the Correlation Analysis............................................. -
The Articulatory and Acoustic Characteristics of Polish Sibilants and Their Consequences for Diachronic Change
The articulatory and acoustic characteristics of Polish sibilants and their consequences for diachronic change Veronique´ Bukmaier & Jonathan Harrington Institute of Phonetics and Speech Processing, University of Munich, Germany [email protected] [email protected] The study is concerned with the relative synchronic stability of three contrastive sibilant fricatives /s ʂɕ/ in Polish. Tongue movement data were collected from nine first-language Polish speakers producing symmetrical real and non-word CVCV sequences in three vowel contexts. A Gaussian model was used to classify the sibilants from spectral information in the noise and from formant frequencies at vowel onset. The physiological analysis showed an almost complete separation between /s ʂɕ/ on tongue-tip parameters. The acoustic analysis showed that the greater energy at higher frequencies distinguished /s/ in the fricative noise from the other two sibilant categories. The most salient information at vowel onset was for /ɕ/, which also had a strong palatalizing effect on the following vowel. Whereas either the noise or vowel onset was largely sufficient for the identification of /s ɕ/ respectively, both sets of cues were necessary to separate /ʂ/ from /s ɕ/. The greater synchronic instability of /ʂ/ may derive from its high articulatory complexity coupled with its comparatively low acoustic salience. The data also suggest that the relatively late stage of /ʂ/ acquisition by children may come about because of the weak acoustic information in the vowel for its distinction from /s/. 1 Introduction While there have been many studies in the last 30 years on the acoustic (Evers, Reetz & Lahiri 1998, Jongman, Wayland & Wong 2000,Nowak2006, Shadle 2006, Cheon & Anderson 2008, Maniwa, Jongman & Wade 2009), perceptual (McGuire 2007, Cheon & Anderson 2008,Li et al. -
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis Jan Skoglund1, Jean-Marc Valin2∗ 1Google, San Francisco, CA, USA 2Amazon, Palo Alto, CA, USA [email protected], [email protected] Abstract learned representation set [11]. A typical WaveNet configura- The voice mode of the Opus audio coder can compress wide- tion requires a very high algorithmic complexity, in the order band speech at bit rates ranging from 6 kb/s to 40 kb/s. How- of hundreds of GFLOPS, along with a high memory usage to ever, Opus is at its core a waveform matching coder, and as the hold the millions of model parameters. Combined with the high rate drops below 10 kb/s, quality degrades quickly. As the rate latency, in the hundreds of milliseconds, this renders WaveNet reduces even further, parametric coders tend to perform better impractical for a real-time implementation. Replacing the di- than waveform coders. In this paper we propose a backward- lated convolutional networks with recurrent networks improved compatible way of improving low bit rate Opus quality by re- memory efficiency in SampleRNN [12], which was shown to be synthesizing speech from the decoded parameters. We compare useful for speech coding in [13]. WaveRNN [14] also demon- two different neural generative models, WaveNet and LPCNet. strated possibilities for synthesizing at lower complexities com- WaveNet is a powerful, high-complexity, and high-latency ar- pared to WaveNet. Even lower complexity and real-time opera- chitecture that is not feasible for a practical system, yet pro- tion was recently reported using LPCNet [15]. vides a best known achievable quality with generative models. -
Lecture 16: Linear Prediction-Based Representations
Return to Main LECTURE 16: LINEAR PREDICTION-BASED REPRESENTATIONS Objectives ● Objectives: System View: Analysis/Synthesis ❍ Introduce analysis/synthesis systems The Error Signal ❍ Discuss error analysis and gain-matching Gain Matching ❍ Relate linear prediction coefficients to other spectral representations Transformations: Levinson-Durbin ❍ Introduce reflection and prediction coefficent recursions Reflection Coefficients Transformations ❍ Lattice/ladder filter implementations The Burg Method There is a classic textbook on this subject: Summary: Signal Modeling J.D. Markel and A.H. Gray, Linear Prediction of Speech, Springer-Verlag, New Typical Front End York, New York, USA, ISBN: 0-13-007444-6, 1976. On-Line Resources: This lecture also includes material from two other textbooks: AJR: Linear Prediction LPC10E MAD LPC J. Deller, et. al., Discrete-Time Processing of Speech Signals, MacMillan Publishing Co., ISBN: 0-7803-5386-2, 2000. Filter Design and, L.R. Rabiner and B.W. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Upper Saddle River, New Jersey, USA, ISBN: 0-13-015157-2, 1993. Return to Main Introduction: 01: Organization (html, pdf) Speech Signals: ECE 8463: FUNDAMENTALS OF SPEECH 02: Production (html, pdf) RECOGNITION 03: Digital Models Professor Joseph Picone (html, pdf) Department of Electrical and Computer Engineering Mississippi State University 04: Perception (html, pdf) email: [email protected] phone/fax: 601-325-3149; office: 413 Simrall 05: Masking URL: http://www.isip.msstate.edu/resources/courses/ece_8463 (html, pdf) Modern speech understanding systems merge interdisciplinary technologies from Signal Processing, 06: Phonetics and Phonology Pattern Recognition, Natural Language, and Linguistics into a unified statistical framework. These (html, pdf) systems, which have applications in a wide range of signal processing problems, represent a revolution in Digital Signal Processing (DSP). -
Source-Channel Coding for CELP Speech Coders
2G'S c5 Source-Channel Coding for CELP Speech Coders J.A. Asenstorfer B.Ma.Sc. (Hons),B.Sc.(Hons),8.8',M'E' University of Adelaide Department of Electrical and Electronic Engineering Thesis submitted for the Degree of Doctor of Philosophy. Septemb er 26, L994 A*ond p.] l'ìq t Contents 1 1 Speech Coders for NoisY Channels 1.1 Introduction 1 2 I.2 Thesis Goals and Historical Perspective 2 Linear Predictive Coders Background I I 2.1 Introduction ' 10 2.2 Linear Predictive Models . 2.3 Autocorrelation of SPeech 15 17 2.4 Generic Code Excited Linear Prediction 3 Pitch Estimation Based on Chaos Theory 23 23 3.1 Introduction . 3.1.1 A Novel Approach to Pitch Estimation 25 3.2 The Non-Linear DYnamical Model 26 29 3.3 Determining the DelaY 32 3.4 Speech Database 3.5 Initial Findings 32 3.5.1 Poincaré Sections 34 35 3.5.2 Poincaré Section in Time 39 3.6 SpeechClassifrcation 40 3.6.1 The Zero Set 4l 3.6.2 The Devil's Staircase 42 3.6.3 Orbit Direction Change Indicator 45 3.7 Voicing Estimator 47 3.8 Pitch Tracking 49 3.9 The Pitch Estimation Algorithm ' 51 3.10 Results from the Pitch Estimator 55 3.11 Waveform Substitution 58 3.11.1 Replication of the Last Pitch Period 59 3.1I.2 Unvoiced Substitution 60 3.11.3 SPlicing Coders 61 3.11.4 Memory Considerations for CELP 61 3.12 Conclusion Data Problem 63 4 Vector Quantiser and the Missing 63 4.I Introduction bÐ 4.2 WhY Use Vector Quantisation? a Training Set 66 4.2.1 Generation of a Vector Quantiser Using Results 68 4.2.2 Some Useful Optimal Vector Quantisation ,7r 4.3 Heuristic Argument ùn . -
Detection and Classification of Whale Acoustic Signals
Detection and Classification of Whale Acoustic Signals by Yin Xian Department of Electrical and Computer Engineering Duke University Date: Approved: Loren Nolte, Supervisor Douglas Nowacek (Co-supervisor) Robert Calderbank (Co-supervisor) Xiaobai Sun Ingrid Daubechies Galen Reeves Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the Graduate School of Duke University 2016 Abstract Detection and Classification of Whale Acoustic Signals by Yin Xian Department of Electrical and Computer Engineering Duke University Date: Approved: Loren Nolte, Supervisor Douglas Nowacek (Co-supervisor) Robert Calderbank (Co-supervisor) Xiaobai Sun Ingrid Daubechies Galen Reeves An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the Graduate School of Duke University 2016 Copyright c 2016 by Yin Xian All rights reserved except the rights granted by the Creative Commons Attribution-Noncommercial Licence Abstract This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase sig- nals, we can represent the whale calls by their polynomial phase coefficients. -
Samia Dawood Shakir
MODELLING TALKING HUMAN FACES Thesis submitted to Cardiff University in candidature for the degree of Doctor of Philosophy. Samia Dawood Shakir School of Engineering Cardiff University 2019 ii DECLARATION This work has not been submitted in substance for any other degree or award at this or any other university or place of learning, nor is being submitted concurrently in candidature for any degree or other award. Signed . (candidate) Date . STATEMENT 1 This thesis is being submitted in partial fulfillment of the requirements for the degree of PhD. Signed . (candidate) Date . STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated, and the thesis has not been edited by a third party beyond what is permitted by Cardiff Universitys Policy on the Use of Third Party Editors by Research Degree Students. Other sources are acknowledged by explicit references. The views expressed are my own. Signed . (candidate) Date . STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available online in the Universitys Open Access repository and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed . (candidate) Date . ABSTRACT This thesis investigates a number of new approaches for visual speech synthesis using data-driven methods to implement a talking face. The main contributions in this thesis are the following. The ac- curacy of shared Gaussian process latent variable model (SGPLVM) built using the active appearance model (AAM) and relative spectral transform-perceptual linear prediction (RASTAPLP) features is im- proved by employing a more accurate AAM. -
Music Information Retrieval Using Social Tags and Audio
A Survey of Audio-Based Music Classification and Annotation Zhouyu Fu, Guojun Lu, Kai Ming Ting, and Dengsheng Zhang IEEE Trans. on Multimedia, vol. 13, no. 2, April 2011 presenter : Yin-Tzu Lin (阿孜孜^.^) 2011/08 Types of Music Representation • Music Notation – Scores – Like text with formatting symbolic • Time-stamped events – E.g. Midi – Like unformatted text • Audio – E.g. CD, MP3 – Like speech 2 Image from: http://en.wikipedia.org/wiki/Graphic_notation Inspired by Prof. Shigeki Sagayama’s talk and Donald Byrd’s slide Intra-Song Info Retrieval Composition Arrangement probabilistic Music Theory Symbolic inverse problem Learning Score Transcription MIDI Conversion Accompaniment Melody Extraction Performer Structural Segmentation Synthesize Key Detection Chord Detection Rhythm Pattern Tempo/Beat Extraction Modified speed Onset Detection Modified timbre Modified pitch Separation Audio 3 Inspired by Prof. Shigeki Sagayama’s talk Inter-Song Info Retrieval • Generic-similar – Music Classification • Genre, Artist, Mood, Emotion… Music Database – Tag Classification(Music Annotation) – Recommendation • Specific-similar – Query by Singing/Humming – Cover Song Identification – Score Following 4 Classification Tasks • Genre Classification • Mood Classification • Artist Identification • Instrument Recognition • Music Annotation 5 Paper Outline • Audio Features – Low-level features – Middle-level features – Song-level feature representations • Classifiers Learning • Classification Task • Future Research Issues 6 Audio Features 7 Low-level Features -
End-To-End Video-To-Speech Synthesis Using Generative Adversarial Networks
1 End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks Rodrigo Mira, Konstantinos Vougioukas, Pingchuan Ma, Stavros Petridis Member, IEEE Bjorn¨ W. Schuller Fellow, IEEE, Maja Pantic Fellow, IEEE Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video, and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm. Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech, which is then fed to a waveform critic and a power critic. The use of an adversarial loss based on these two critics enables the direct synthesis of raw audio waveform and ensures its realism. In addition, the use of our three comparative losses helps establish direct correspondence between the generated audio and the input video. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID, and that it is the first end-to-end model to produce intelligible speech for LRW (Lip Reading in the Wild), featuring hundreds of speakers recorded entirely ‘in the wild’. We evaluate the generated samples in two different scenarios – seen and unseen speakers – using four objective metrics which measure the quality and intelligibility of artificial speech. -
Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments
International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013 Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments D. M. Chandwadkar and M. S. Sutaone y The performance of the learning procedure is evaluated Abstract—Selection of effective feature set and proper by classifying new sound samples (cross-validation). classifier is a challenging task in problems where machine One of the most crucial aspects in the above procedure of learning techniques are used. In automatic identification of instrument classification is to find the right features [2] and musical instruments also it is very crucial to find the right set of classifiers. Most of the research on audio signal processing features and accurate classifier. In this paper, the role of various features with different classifiers on automatic has been focusing on speech recognition and speaker identification of musical instruments is discussed. Piano, identification. Few features used for these can be directly acoustic guitar, xylophone and violin are identified using applied to solve the instrument classification problem. various features and classifiers. Spectral features like spectral In this paper, feature extraction and selection for centroid, spectral slope, spectral spread, spectral kurtosis, instrument classification using machine learning techniques spectral skewness and spectral roll-off are used along with autocorrelation coefficients and Mel Frequency Cepstral is considered. Four instruments: piano, acoustic guitar, Coefficients (MFCC) for this purpose. The dependence of xylophone and violin are identified using various features instrument identification accuracy on these features is studied and classifiers. A number of spectral features, MFCCs, and for different classifiers. Decision trees, k nearest neighbour autocorrelation coefficients are used. -
Linear Predictive Modelling of Speech - Constraints and Line Spectrum Pair Decomposition
Helsinki University of Technology Laboratory of Acoustics and Audio Signal Processing Espoo 2004 Report 71 LINEAR PREDICTIVE MODELLING OF SPEECH - CONSTRAINTS AND LINE SPECTRUM PAIR DECOMPOSITION Tom Bäckström Dissertation for the degree of Doctor of Science in Technology to be presented with due permission for public examination and debate in Auditorium S4, Department of Electrical and Communications Engineering, Helsinki University of Technology, Espoo, Finland, on the 5th of March, 2004, at 12 o'clock noon. Helsinki University of Technology Department of Electrical and Communications Engineering Laboratory of Acoustics and Audio Signal Processing Teknillinen korkeakoulu Sähkö- ja tietoliikennetekniikan osasto Akustiikan ja äänenkäsittelytekniikan laboratorio Helsinki University of Technology Laboratory of Acoustics and Audio Signal Processing P.O. Box 3000 FIN-02015 HUT Tel. +358 9 4511 Fax +358 9 460 224 E-mail [email protected] ISBN 951-22-6946-5 ISSN 1456-6303 Otamedia Oy Espoo, Finland 2004 Let the floor be the limit! HELSINKI UNIVERSITY OF TECHNOLOGY ABSTRACT OF DOCTORAL DISSERTATION P.O. BOX 1000, FIN-02015 HUT http://www.hut.fi Author Tom Bäckström Name of the dissertation Linear predictive modelling for speech - Constraints and Line Spectrum Pair Decomposition Date of manuscript 6.2.2004 Date of the dissertation 5.3.2004 Monograph 4 Article dissertation (summary + original articles) Department Electrical and Communications Engineering Laboratory Laboratory Acoustics and Audio Signal Processing Field of research Speech processing Opponent(s) Associate Professor Peter Händel Supervisor Professor Paavo Alku (Instructor) Abstract In an exploration of the spectral modelling of speech, this thesis presents theory and applications of constrained linear predictive (LP) models.