Analyzing Sounds of Home Environment for Device Recognition

Dimitrov, Svilen; Britz, J.; Brandherm, Boris; Frey, Jochen

doi:10.1007/978-3-319-14112-1_1

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Weakly labelled audio data contains only the presence or absence of the audio events but without the time stamps of the audio events [1]. Weakly labelled audio classification has many applications in information retrieval [2], surveillance of abnormal sound in public area and industry use [3]. Some challenges divide the audio classification to subtasks including audio scene classification [4] and sound event detection [4].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

Kong

Wang

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

183

184

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In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labelled with one or a few audio tags but without time stamps of the audio events, which is called as weakly labelled data. Two subtasks are defined in this challenge including audio tagging and sound event detection using this weakly labelled data. A convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram is proposed.In addition, a temporal attention method is proposed along the frames to predicate the locations of each audio event in a chunk from the weakly labelled data. We ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6% and Equal error 0.73, respectively.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

Kong

Wang

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

183

184

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“…Audio tagging (AT) refers to the task of assigning labels of one or several sound classes to an audio recording. Potential applications of audio tagging include sound event detection [1], audio retrieval [2] and audio surveillance [3,4]. There is an increasing interest in audio tagging research, including the collection of training data covering a large number of event classes, and development of learning algorithms for classification.…”

Section: Introductionmentioning

confidence: 99%

Gated Multi-Head Attention Pooling for Weakly Labelled Audio Tagging

Hong¹,

Zou²,

Wang³

2020

Interspeech 2020

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Multiple instance learning (MIL) has recently been used for weakly labelled audio tagging, where the spectrogram of an audio signal is divided into segments to form instances in a bag, and then the low-dimensional features of these segments are pooled for tagging. The choice of a pooling scheme is the key to exploiting the weakly labelled data. However, the traditional pooling schemes are usually fixed and unable to distinguish the contributions, making it difficult to adapt to the characteristics of the sound events. In this paper, a novel pooling algorithm is proposed for MIL, named gated multi-head attention pooling (GMAP), which is able to attend to the information of events from different heads at different positions. Each head allows the model to learn information from different representation subspaces. Furthermore, in order to avoid the redundancy of multi-head information, a gating mechanism is used to fuse individual head features. The proposed GMAP increases the modeling power of the single-head attention with no computational overhead. Experiments are carried out on Audioset, which is a large-scale weakly labelled dataset, and show superior results to the non-adaptive pooling and the vanilla attention pooling schemes.

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“…Audio clips are typically short segments such as 10 seconds of a long recording. Audio tagging has many applications in information retrieval [1], audio classification [2], acoustic scene recognition [3] and industry sound recognition [4].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with weak tags, the order of tags is known in SLD. In addition, the order information of events will benefit tasks like acoustic scene analysis [3] and environment recognition [4]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Audio Tagging With Connectionist Temporal Classification Model Using Sequentially Labelled Data

Hou

Kong

2019

Lecture Notes in Electrical Engineering

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Audio tagging aims to predict one or several labels in an audio clip. Many previous works use weakly labelled data (WLD) for audio tagging, where only presence or absence of sound events is known, but the order of sound events is unknown. To use the order information of sound events, we propose sequential labelled data (SLD), where both the presence or absence and the order information of sound events are known. To utilize SLD in audio tagging, we propose a Convolutional Recurrent Neural Network followed by a Connectionist Temporal Classification (CRNN-CTC) objective function to map from an audio clip spectrogram to SLD. Experiments show that CRNN-CTC obtains an Area Under Curve (AUC) score of 0.986 in audio tagging, outperforming the baseline CRNN of 0.908 and 0.815 with Max Pooling and Average Pooling, respectively. In addition, we show CRNN-CTC has the ability to predict the order of sound events in an audio clip.

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Analyzing Sounds of Home Environment for Device Recognition

Cited by 4 publications

References 18 publications

Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

Gated Multi-Head Attention Pooling for Weakly Labelled Audio Tagging

Audio Tagging With Connectionist Temporal Classification Model Using Sequentially Labelled Data

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