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Machine audition is the study of algorithms and systems for the automatic analysis and understanding of sound by machine. It has recently attracted increasing interest within several research communities, such as signal processing, machine learning, auditory modeling, perception and cognition, psychology, pattern recognition, and artificial intelligence. However, the developments made so far are fragmented within these disciplines, lacking connections and incurring potentially overlapping research activities in this subject area. Machine Audition: Principles, Algorithms and Systems contains advances in algorithmic developments, theoretical frameworks, and experimental research findings. This book is useful for professionals who want an improved understanding about how to design algorithms for performing automatic analysis of audio signals, construct a computing system for understanding sound, and learn how to build advanced human-computer interactive systems.
The Advocate is a lesbian, gay, bisexual, transgender (LGBT) monthly newsmagazine. Established in 1967, it is the oldest continuing LGBT publication in the United States.
The Advocate is a lesbian, gay, bisexual, transgender (LGBT) monthly newsmagazine. Established in 1967, it is the oldest continuing LGBT publication in the United States.
This book is a printed edition of the Special Issue "Sound and Music Computing" that was published in Applied Sciences
Market research guide to American employers. Includes hard-to-find information such as benefit plans, stock plans, salaries, hiring and recruiting plans, training and corporate culture, growth plans. Several indexes and tables, as well as a job market trends analysis and 7 Keys For Research for job openings. This massive reference book features our proprietary profiles of the 500 best, largest, and fastest-growing corporate employers in America--includes addresses, phone numbers, and Internet addresses.
Presents information on 4-year colleges and universities and 2-year community colleges and technical schools.
The brand-new, totally updated edition of the college guide covering every aspect of campus life. Complete profiles of the more than 1,500 accredited four-year colleges and universities in the United States, Canada, and Mexico, cover everything from admissions to prominent graduates to social events.
This book presents the methods, tools and techniques that are currently being used to recognise (automatically) the affect, emotion, personality and everything else beyond linguistics (‘paralinguistics’) expressed by or embedded in human speech and language. It is the first book to provide such a systematic survey of paralinguistics in speech and language processing. The technology described has evolved mainly from automatic speech and speaker recognition and processing, but also takes into account recent developments within speech signal processing, machine intelligence and data mining. Moreover, the book offers a hands-on approach by integrating actual data sets, software, and open-source utilities which will make the book invaluable as a teaching tool and similarly useful for those professionals already in the field. Key features: Provides an integrated presentation of basic research (in phonetics/linguistics and humanities) with state-of-the-art engineering approaches for speech signal processing and machine intelligence. Explains the history and state of the art of all of the sub-fields which contribute to the topic of computational paralinguistics. C overs the signal processing and machine learning aspects of the actual computational modelling of emotion and personality and explains the detection process from corpus collection to feature extraction and from model testing to system integration. Details aspects of real-world system integration including distribution, weakly supervised learning and confidence measures. Outlines machine learning approaches including static, dynamic and context‐sensitive algorithms for classification and regression. Includes a tutorial on freely available toolkits, such as the open-source ‘openEAR’ toolkit for emotion and affect recognition co-developed by one of the authors, and a listing of standard databases and feature sets used in the field to allow for immediate experimentation enabling the reader to build an emotion detection model on an existing corpus.