Acoustic Scene Classification Using Spatial Pyramid Pooling with Convolutional Neural Networks

Basbug, Ahmet Melih; Sert, Mustafa

doi:10.1109/icosc.2019.8665547

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…Several extensions to the common CNN architecture were proposed to improve the feature learning. Basbug and Sert adapted the spatial pyramid pooling strategy from computer vision, where feature maps are pooled and combined on different spatial resolutions [59]. In order to learn frequency-aware filters in the convolutional layers, Koutini et al proposed to encode the frequency position of each input feature bin within an additional channel dimension (frequency-aware CNNs) [44].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer

2020

Applied Sciences

129

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The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Abeßer

2020

Applied Sciences

129

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“…In the process of object detection and recognition, SPP (Basbug and Sert 2019;Grauman and Darrell 2005;Lazebnik et al 2006) has been surprisingly effective. Despite its simplicity, SPP is competitive with methods that use more complex spatial paradigms.…”

Section: Spatial Pyramid Pooling (Spp) Networkmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

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An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.

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“…Previous studies show that VGGish embeddings achieve good results compared with hand-crafted audio features in audio classification tasks [24,27]. In order to extract audio embedding, we first extract the log Mel spectrograms from audio clips.…”

Section: Audio Embeddingsmentioning

confidence: 99%

Audio Captioning with Composition of Acoustic and Semantic Information

Eren

Sert

2021

Int. J. Semantic Computing

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Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder–decoder-based models without considering semantic information. To fill this gap, we present a novel encoder–decoder architecture using bi-directional Gated Recurrent Units (BiGRU) with audio and semantic embeddings. We extract semantic embedding by obtaining subjects and verbs from the audio clip captions and combine these embedding with audio embedding to feed the BiGRU-based encoder–decoder model. To enable semantic embeddings for the test audios, we introduce a Multilayer Perceptron classifier to predict the semantic embeddings of those clips. We also present exhaustive experiments to show the efficiency of different features and datasets for our proposed model the audio captioning task. To extract audio features, we use the log Mel energy features, VGGish embeddings, and a pretrained audio neural network (PANN) embeddings. Extensive experiments on two audio captioning datasets Clotho and AudioCaps show that our proposed model outperforms state-of-the-art audio captioning models across different evaluation metrics and using the semantic information improves the captioning performance.

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Acoustic Scene Classification Using Spatial Pyramid Pooling with Convolutional Neural Networks

Cited by 23 publications

References 8 publications

A Review of Deep Learning Based Methods for Acoustic Scene Classification

A Review of Deep Learning Based Methods for Acoustic Scene Classification

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Audio Captioning with Composition of Acoustic and Semantic Information

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