Music information retrieval is of great interest in audio signal processing. However, relatively little attention has been paid to the playing techniques of musical instruments. This work proposes an automatic system for classifying guitar playing techniques (GPTs). Automatic classification for GPTs is challenging because some playing techniques differ only slightly from others. This work presents a new framework for GPT classification: it uses a new feature extraction method based on spectral-temporal receptive fields (STRFs) to extract features from guitar sounds. This work applies a supervised deep learning approach to classify GPTs. Specifically, a new deep learning model, called the hierarchical cascade deep belief network (HCDBN), is proposed to perform automatic GPT classification. Several simulations were performed and the datasets of: 1) data on onsets of signals; 2) complete audio signals; and 3) audio signals in a real-world environment are adopted to compare the performance. The proposed system improves upon the F-score by approximately 11.47% in setup 1) and yields an F-score of 96.82% in setup 2). The results in setup 3) demonstrate that the proposed system also works well in a real-world environment. These results show that the proposed system is robust and has very high accuracy in automatic GPT classification.

This article proposes two novel deep convolutional neural networks (CNN), which are called the sparse coding convolutional neural network (SC-CNN) and the multi-convolutional-channel SC-CNN (MSC-CNN), to address the sound event recognition and retrieval problem. Unlike the general framework of a CNN, in which the feature learning process is performed hierarchically, the proposed framework models the whole memorization process in the human brain, including encoding, storage, and recollection. In particular, the MSC-CNN is designed to recognize multiple sound events that occur simultaneously. The experimental results indicate that the proposed SC-CNN and MSC-CNN outperforms the state-of-the-art systems in sound event recognition and retrieval.

Automatic sound event recognition (SER) has recently attracted renewed interest. Although practical SER system has many useful applications in everyday life, SER is challenging owing to the variations among sounds and noises in the real-world environment. This paper presents a novel feature extraction and classification method to solve the problem of SER. An audio-visual descriptor, called the auditory-receptive-field binary pattern, is designed based on the spectrogram image feature, the cepstral features, and the human auditory receptive field model. The extracted features are then fed into a classifier to perform event classification. The proposed classifier, called the hierarchical-diving deep belief network, is a deep neural network system that hierarchically learns the discriminative characteristics from physical feature representation to the abstract concept. The performance of our proposed system was verified using several experiments under various conditions. Using the RWCP dataset, the proposed system achieved a recognition rate of 99.27% for real-world sound data in 105 categories. Under noisy conditions, the developed system is very robust, with which it achieved 95.06% recognition rate with 0 dB signal-to-noise ratio. Using the TUT sound event dataset, the proposed system achieves error rates of 0.81 and 0.73 in sound event detection in home and residential area scenes. The experimental results reveal that the proposed system outperformed the other systems in this field.

This paper proposes a novel deep convolutional neural network (CNN), called sparse coding convolutional neural network (SC-CNN), to address the problem of sound event recognition and retrieval task. Unlike the general framework of a CNN, in which feature learning process is performed hierarchically, the proposed framework models the whole memorizing procedures in the human brain, including encoding, storage, and recollection. Sound data from the RWCP sound scene dataset with added noise from NOISEX-92 noise dataset are used to compare the performance of the proposed system with the state-of-the-art baselines. The experimental results indicated that the proposed SC-CNN outperformed the state-of-the-art systems in sound event recognition and retrieval. In the sound event recognition task, the proposed system achieved an accuracy of 94.6%, 100% and 100% under 0db, 10db and clean noise conditions, respectively. In the retrieval task, the proposed system improves the mAP rate of the general CNN by approximately 6%.

In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words and then pool them together with some weights to construct the feature representation vector of an image. In the classification step, kernel sparse representation classifier is used to address the problem of visual-based vehicle classification. The kernel function maps the features from original space into higher space dimension. The modified active-set algorithm for l1 non-negative least square problem is adopted to solve the optimization problem. The experimental results show the improvement of proposed method over the original SPM. The proposed method can achieve the performance of 93.7% using particular vehicle image dataset.