Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors

Atlas, Les; Bernard, Gary D.; Narayanan, S.

doi:10.1109/5.535250

Cited by 57 publications

(27 citation statements)

References 23 publications

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“…In machining parts, for example, the common industrial practice of replacing cutters according to a fixed schedule based on average cutter life is problematic and/or inefficient because of the wide variation in usable cutter life. For this reason, a substantial amount of research has gone into the field of automatic monitoring and control of machining processes [6,7]. Research has focused on developing sensors, feature extraction methods, and automatic classification techniques for predicting when a tool is dull and needs to be replaced.…”

Section: Case Study: Tool Wear Monitoringmentioning

confidence: 99%

Joint Use of Dynamical Classifiers and Ambiguity Plane Features

Ostendorf¹,

Atlas²,

Fish³

et al. 2001

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This paper argues for using ambiguity plane features within dynamic statistical models for classification problems. The relative contribution of the two model components are investigated in the context of acoustically monitoring cutter wear during milling of titanium, an application where it is known that standard static classification techniques work poorly. Experiments show that explicit modeling of long-term context via a hidden Markov model state improves performance, but mainly by using this to augment sparsely labeled training data. An additional performance gain is achieved by using the shorter-term context of ambiguity plane features.

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Section: Case Study: Tool Wear Monitoringmentioning

confidence: 99%

Joint Use of Dynamical Classifiers and Ambiguity Plane Features

Ostendorf¹,

Atlas²,

Fish³

et al. 2001

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“…Examples include gunshots [16], vehicles [17], machines [18], and birds [19]. In addition to this, usually a low number of sound categories are involved in the studies, specifically chosen to minimize overlapping between different categories, and evaluations are carried out with one or very small set of audio contexts (kitchen [20], bathroom [21], meeting room [22], office and canteen [23]).…”

Section: Previous Workmentioning

confidence: 99%

Context-dependent sound event detection

Heittola

Mesaros

Eronen

et al. 2013

J AUDIO SPEECH MUSIC PROC.

200

137

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The work presented in this article studies how the context information can be used in the automatic sound event detection process, and how the detection system can benefit from such information. Humans are using context information to make more accurate predictions about the sound events and ruling out unlikely events given the context. We propose a similar utilization of context information in the automatic sound event detection process. The proposed approach is composed of two stages: automatic context recognition stage and sound event detection stage. Contexts are modeled using Gaussian mixture models and sound events are modeled using three-state left-to-right hidden Markov models. In the first stage, audio context of the tested signal is recognized. Based on the recognized context, a context-specific set of sound event classes is selected for the sound event detection stage. The event detection stage also uses context-dependent acoustic models and count-based event priors. Two alternative event detection approaches are studied. In the first one, a monophonic event sequence is outputted by detecting the most prominent sound event at each time instance using Viterbi decoding. The second approach introduces a new method for producing polyphonic event sequence by detecting multiple overlapping sound events using multiple restricted Viterbi passes. A new metric is introduced to evaluate the sound event detection performance with various level of polyphony. This combines the detection accuracy and coarse time-resolution error into one metric, making the comparison of the performance of detection algorithms simpler. The two-step approach was found to improve the results substantially compared to the context-independent baseline system. In the block-level, the detection accuracy can be almost doubled by using the proposed context-dependent event detection.

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“…음향 사건 검출과 관련된 초기 연구는 특정한 단일 음향 사건들, 예컨대 비명, [6] 총소리, [7] 기계음, [8] 음성/ 음악 전환, [9] 기침 [10] 등을 검출하는데 집중하였다. 이러 한 기술들은 감시 [6∼7] 나 멀티미디어 검색 [11] [2,5] 혹은 우도비 검정(likelihood ratio test) [6] 을 통하여 해당 사건으로 판별된다.…”

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Dual-Channel Acoustic Event Detection in Multisource Environments Using Nonnegative Tensor Factorization and Hidden Markov Model

Jeon¹,

Kim²

2017

Journal of the Institute of Electronics and Information Enginee

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In this paper, we propose a dual-channel acoustic event detection (AED) method using nonnegative tensor factorization (NTF) and hidden Markov model (HMM) in order to improve detection accuracy of AED in multisource environments. The proposed method first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique applied to dual-channel input signals. After that, an HMM-based likelihood ratio test is carried out to verify the detected events by using channel gains. The detection accuracy of the proposed method is measured by F-measures under 9 different multisource conditions. Then, it is also compared with those of conventional AED methods such as Gaussian mixture model and nonnegative matrix factorization. It is shown from the experiments that the proposed method outperforms the convectional methods under all the multisource conditions.

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Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors

Cited by 57 publications

References 23 publications

Joint Use of Dynamical Classifiers and Ambiguity Plane Features

Joint Use of Dynamical Classifiers and Ambiguity Plane Features

Context-dependent sound event detection

Dual-Channel Acoustic Event Detection in Multisource Environments Using Nonnegative Tensor Factorization and Hidden Markov Model

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