Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency. Advanced preprocessing and classification of data at the network edge can help to decrease the communication demand and to reduce the amount of data to be processed centrally. In the area of distributed acoustic sensing, the combination of algorithms with a high classification rate and resource-constraint embedded systems is essential. Unfortunately, algorithms for acoustic event detection have a high memory and computational demand and are not suited for execution at the network edge. This paper addresses these aspects by applying structural optimizations to a convolutional neural network for audio event detection to reduce the memory requirement by a factor of more than 500 and the computational effort by a factor of 2.1 while performing 9.2 better.
For acoustic event detection different algorithms have been presented based on Non-Negative Matrix Factorization @cite_2 , Hidden Markov Models @cite_16 or Recurrent Neural Networks @cite_10 . Like in many other machine learning applications, CNNs have been proven to be the key for high classification accuracy. The advantage of such an architecture for acoustic event detection is its inherent inclusion of a temporal neighborhood since acoustic events are strongly characterized by temporal changes. Besides the mentioned CNN for acoustic event detection, so far CNNs have been used mainly for speech recognition @cite_11 or music @cite_7 classification tasks. These algorithms are all computationally expensive, and the current state-of-the-art also comes at the expense of having a huge number of parameters @cite_3 . Implementing such a network on mobile devices or sensor nodes is difficult due to memory and computational restrictions on these devices. A way to reduce the complexity of CNN-based classification systems has been presented for image classification @cite_9 and a similar structure is used in @cite_14 .
@cite_14 @cite_7 @cite_9 @cite_3 @cite_2 @cite_16 @cite_10 @cite_11
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Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks CNNs are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks RNNs are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a convolutional recurrent neural network CRNN and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events. Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task. Despite significant accuracy improvement in convolutional neural networks (CNN) based object detectors, they often require prohibitive runtimes to process an image for real-time applications. State-of-the-art models often use very deep networks with a large number of floating point operations. Efforts such as model compression learn compact models with fewer number of parameters, but with much reduced accuracy. In this work, we propose a new framework to learn compact and fast object detection networks with improved accuracy using knowledge distillation [20] and hint learning [34]. Although knowledge distillation has demonstrated excellent improvements for simpler classification setups, the complexity of detection poses new challenges in the form of regression, region proposals and less voluminous labels. We address this through several innovations such as a weighted cross-entropy loss to address class imbalance, a teacher bounded loss to handle the regression component and adaptation layers to better learn from intermediate teacher distributions. We conduct comprehensive empirical evaluation with different distillation configurations over multiple datasets including PASCAL, KITTI, ILSVRC and MS-COCO. Our results show consistent improvement in accuracy-speed trade-offs for modern multi-class detection models. We propose a new blind segmentation approach to acoustic event detection (AED) based on i-vectors. Conventional approaches to AED often required well-segmented data with non-overlapping boundaries for competing events. Inspired by block-based automatic image annotation in image retrieval tasks, we blindly segment audio streams into equal-length pieces, label the underlying observed acoustic events with multiple categories and with no event boundary information, extract i-vector for them, and perform classification using support vector machine and maximal figure-of-merit based classifiers. Experiments on various sets of audio data show promising results with an average of 8 absolute gain in F1 over the conventional hidden Markov model based approach. An enhanced robustness at different noise levels is also observed. The key to the success lies in the enhanced discrimination power offered by the i-vector representation of the acoustic data. Index Terms: acoustic event detection, i-vector, blind segmentation, support vector machine, maximal figure-of-merit