Automatic classification of environmental noise events by hidden Markov models,” in (1998)

by P Gaunard, C G Mubikangiey, C Couvreur, V Fontaine
Venue:Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP),
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Hidden Markov processes

by Yariv Ephraim, Neri Merhav - IEEE Trans. Inform. Theory , 2002
"... Abstract—An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie on finite- ..."
Abstract - Cited by 264 (5 self) - Add to MetaCart
Abstract—An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed in this paper. Index Terms—Baum–Petrie algorithm, entropy ergodic theorems, finite-state channels, hidden Markov models, identifiability, Kalman filter, maximum-likelihood (ML) estimation, order estimation, recursive parameter estimation, switching autoregressive processes, Ziv inequality. I.
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Computational Auditory Scene Recognition

by Vesa Peltonen, Vesa Peltonen - In IEEE Int’l Conf. on Acoustics, Speech, and Signal Processing , 2001
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Abstract - Cited by 67 (1 self) - Add to MetaCart
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Automatic Musical Instrument Recognition

by Antti Eronen , 2001
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Abstract - Cited by 25 (0 self) - Add to MetaCart
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Acoustic environment classification

by Ling Ma, Ben Milner, Dan Smith - ACM Transactions on Speech and Language Processing , 2006
"... The acoustic environment provides a rich source of information on the types of activity, communication modes, and people involved in many situations. It can be accurately classified using recordings from microphones commonly found in PDAs and other consumer devices. We describe a prototype HMM-based ..."
Abstract - Cited by 21 (0 self) - Add to MetaCart
The acoustic environment provides a rich source of information on the types of activity, communication modes, and people involved in many situations. It can be accurately classified using recordings from microphones commonly found in PDAs and other consumer devices. We describe a prototype HMM-based acoustic environment classifier incorporating an adaptive learning mechanism and a hierarchical classification model. Experimental results show that we can accurately classify a wide variety of everyday environments. We also show good results classifying single sounds, although classification accuracy is influenced by the granularity of the classification.

Recognition of Acoustic Noise Mixtures by Combined Bottom-up and Top-down Processing

by Jukka Sillanpää, Anssi Klapuri, Tuomas Virtanen - In Proceedings of the European Signal Processing Conference EUSIPCO, 2000
"... In this paper, a system is described for the recognition of mixtures of noise sources in acoustic input signals. The problem is approached by utilizing both bottom-up signal analysis and top-down predictions of higher-level models. The developments are made using musical signals as test material. Va ..."
Abstract - Cited by 18 (1 self) - Add to MetaCart
In this paper, a system is described for the recognition of mixtures of noise sources in acoustic input signals. The problem is approached by utilizing both bottom-up signal analysis and top-down predictions of higher-level models. The developments are made using musical signals as test material. Validation experiments are presented both for selfgenerated sound mixtures and for real musical recordings. 1
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Context awareness using environmental noise classification

by L. Ma, D. J. Smith, B. P. Milner - in ISCA EUROSPEECH , 2003
"... Context-awareness is essential to the development of adaptive information systems. Environmental noise can provide a rich source of information about the current context. We describe our approach for automatically sensing and recognising noise from typical environments of daily life, such as office, ..."
Abstract - Cited by 18 (1 self) - Add to MetaCart
Context-awareness is essential to the development of adaptive information systems. Environmental noise can provide a rich source of information about the current context. We describe our approach for automatically sensing and recognising noise from typical environments of daily life, such as office, car and city street. In this paper we present our hidden Markov model based noise classifier. We describe the architecture of the system, compare classification results from the system with human listening tests, and discuss open issues in environmental noise classification for mobile computing. 1.
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Environmental Noise Classification for Context-Aware Applications

by Ling Ma, Dan Smith, Ben Milner - In Proc. EuroSpeech-2003 , 2003
"... Abstract. Context-awareness is essential to the development of adaptive information systems. Much work has been done on developing technologies and systems that are aware of absolute location in space and time; other aspects of context have been relatively neglected. We describe our approach to auto ..."
Abstract - Cited by 10 (1 self) - Add to MetaCart
Abstract. Context-awareness is essential to the development of adaptive information systems. Much work has been done on developing technologies and systems that are aware of absolute location in space and time; other aspects of context have been relatively neglected. We describe our approach to automatically sensing and recognising environmental noise as a contextual cue for context-aware applications. Environmental noise provides much valuable information about a user's current context. This paper describes an approach to classifying the noise context in the typical environments of our daily life, such as the office, car and city street. In this paper we present our hidden Markov model based noise classifier. We describe the architecture of our system, the experimental results, and discuss the open issues in environmental noise classification for mobile computing. 1
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Recognition of Everyday Auditory Scenes: Potentials, Latencies and Cues

by Vesa T. K. Peltonen, Antti J. Eronen, Mikko P. Parviainen, Anssi P. Klapuri - In Proc. 110th Audio Eng. Soc. Convention , 2001
"... A listening test was conducted where the human abilities in recognizing everyday auditory scenes based on binaural recordings were studied. The accuracy, latency, and acoustic cues used by the subjects in the recognition process were analyzed. The average correct recognition rate for 19 subjects w ..."
Abstract - Cited by 9 (5 self) - Add to MetaCart
A listening test was conducted where the human abilities in recognizing everyday auditory scenes based on binaural recordings were studied. The accuracy, latency, and acoustic cues used by the subjects in the recognition process were analyzed. The average correct recognition rate for 19 subjects was 70% for 25 different scenes, and the average recognition time was 20 seconds. In most cases, the test subjects reported that the recognition was based on prominent identified sound events.
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Diagnosing new york city’s noises with ubiquitous data

by Yu Zheng , Tong Liu , Yilun Wang , Yanmin Zhu , Yanchi Liu , Eric Chang - In ACM Ubicomp , 2014
"... ABSTRACT Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the t ..."
Abstract - Cited by 9 (2 self) - Add to MetaCart
ABSTRACT Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the third largest category of complaints in the 311 data. As each complaint about noises is associated with a location, a time stamp, and a fine-grained noise category, such as "Loud Music" or "Construction", the data is actually a result of "human as a sensor" and "crowd sensing", containing rich human intelligence that can help diagnose urban noises. In this paper we infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs). We model the noise situation of NYC with a three dimension tensor, where the three dimensions stand for regions, noise categories, and time slots, respectively. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we recover the noise situation throughout NYC. The information can inform people and officials' decision making. We evaluate our method with four real datasets, verifying the advantages of our method beyond four baselines, such as the interpolation-based approach.
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Robust speech recognition using kpcabased noise classification

by Nattanun Thatphithakkul, Boontee Kruatrachue, Chai Wutiwiwatchai, Sanparith Marukatat, Vataya Boonpiam - C.V. Jawahar Tejo Krishna Chalasani, Anoop M. Namboodiri. Support
"... This paper proposes an environmental noise clas-sification method using kernel principal component analysis (KPCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the iden-tified noise specific acoustic model. The propo ..."
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This paper proposes an environmental noise clas-sification method using kernel principal component analysis (KPCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the iden-tified noise specific acoustic model. The proposed model applies KPCA to a set of noise features such as normalized logarithmic spectrums (NLS), and re-sults from KPCA are used by a support vector ma-chines (SVM) classifier for noise classification. The proposed model is evaluated with 2 groups of envi-ronments. The first group contains a clean environ-ment and 9 types of noisy environments that have been trained in the system. Another group contains other 6 types of noises not trained in the system. Noisy speech is prepared by adding noise signals from JEIDA and NOISEX-92 to the clean speech taken from NECTEC-ATR Thai speech corpus. The pro-posed model shows a promising result when evaluat-ing on the task of phoneme based 640 Thai isolated-word recognition.