Sound event recognition in unstructured environments using spectrogram image processing

Dennis, Jonathan William

doi:10.32657/10356/59272

Cited by 30 publications

(47 citation statements)

References 224 publications

(371 reference statements)

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“…We set up the standard experiment of the robust audio event recognition task similar to current state-of-the-art works [18,1,6] so that the results are comparable. Audio event database.…”

Section: Databasesmentioning

confidence: 99%

“…Noise database. As in [18,1,6], we chose four different environmental noises from NOISEX-92 database [20], including "Destroyer Control Room", "Speech Bable", "Factory Floor 1", and "Jet Cockpit 1". Beside clean signals, we also created noise-corrupted signals by randomly choosing one of four noise signals to add to the clean signals at random starting points.…”

Section: Databasesmentioning

confidence: 99%

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Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

Phan¹,

Hertel²,

Maass³

et al. 2016

Interspeech 2016

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We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it from the deep architectures that have been proposed for the task: varying-size convolutional filters at the convolutional layer and 1-max pooling scheme at the pooling layer. In intuition, the network tends to select the most discriminative features from the whole audio signals for recognition. Our proposed CNN not only shows state-of-the-art performance on the standard task of robust audio event recognition but also outperforms other deep architectures up to 4.5% in terms of recognition accuracy, which is equivalent to 76.3% relative error reduction.

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“…We set up the standard experiment of the robust audio event recognition task similar to current state-of-the-art works [18,1,6] so that the results are comparable. Audio event database.…”

Section: Databasesmentioning

confidence: 99%

Section: Databasesmentioning

confidence: 99%

Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

Phan¹,

Hertel²,

Maass³

et al. 2016

Interspeech 2016

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“…where X t is a column vector containing the absolute amplitude of the DFT frequency bins and T is the number of DFTs extracted from the signal. The absolute amplitude is favored over the logamplitude as it has shown to yield better results for spectrogram image classification in [32] and in our own experiments. The spectrograms are normalized: each frequency bin is divided by the maximum amplitude value contained in a time frame.…”

Section: Feature Extractionmentioning

confidence: 78%

“…For example, histograms of oriented gradients (HOG) were used to perform word recognition [31]. In [32], spectrograms amplitudes are quantized and mapped into a color coded image. Color distributions are then characterized and analyzed.…”

Section: Feature Learning For Speech Analysismentioning

confidence: 99%

“…Using local features in image recognition may lead to an increased robustness to intra-class variation, deformation, view-point, illumination and occlusion [43]. When working with spectrograms, it translates to an increased robustness to noise [22,32]. In [44] the SIFT descriptor was used to detect and encode key-points in spectrogram images of musical pieces to perform genre classification.…”

Section: Feature Learning For Speech Analysismentioning

confidence: 99%

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Feature Learning from Spectrograms for Assessment of Personality Traits

Carbonneau¹,

Granger²,

Attabi³

et al. 2020

IEEE Trans. Affective Comput.

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Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based on feature learning and spectrogram analysis is proposed to simplify the feature extraction process while maintaining a high level of accuracy. The proposed method learns a dictionary of discriminant features from patches extracted in the spectrogram representations of training speech segments. Each speech segment is then encoded using the dictionary, and the resulting feature set is used to perform classification of personality traits. Experiments indicate that the proposed method achieves state-of-the-art results with a significant reduction in complexity when compared to the most recent reference methods. The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 6 parameters can be tuned automatically. In contrast, the simplest reference method uses 4 types of descriptors to which 6 functionals are applied, resulting in over 20 parameters to be tuned.

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Enhancing Bird Migration Studies: Detecting Birdsong in Audio Files Using Convolutional Neural Networks

Honsor,

Gonsor

2024

Lecture Notes on Data Engineering and Communications Technologies

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Sound event recognition in unstructured environments using spectrogram image processing

Cited by 30 publications

References 224 publications

Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

Feature Learning from Spectrograms for Assessment of Personality Traits

Enhancing Bird Migration Studies: Detecting Birdsong in Audio Files Using Convolutional Neural Networks

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