Current Issue : April-June Volume : 2022 Issue Number : 2 Articles : 5 Articles
To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. (en, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. (e dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective....
*e music performance system works by identifying the emotional elements of music to control the lighting changes. However, if there is a recognition error, a good stage effect will not be able to create. *erefore, this paper proposes an intelligent music emotion recognition and classification algorithm in the music performance system. *e first part of the algorithm is to analyze the emotional features of music, including acoustic features, melody features, and audio features. *en, the three kinds of features are combined together to form a feature vector set. In the latter part of the algorithm, it divides the feature vector set into training samples and test samples. *e training samples are trained by using recognition and classification model based on the neural network. And then, the testing samples are input into the trained model, which is aiming to realize the intelligent recognition and classification of music emotion. *e result shows that the kappa coefficient k values calculated by the proposed algorithm are greater than 0.75, which indicates that the recognition and classification results are consistent with the actual results, and the accuracy of recognition and classification is high. So, the research purpose is achieved....
With the explosive growth of voice information interaction, there is an urgent need for safe and effective compression transmission methods. In this paper, compressive sensing is used to realize the compression and encryption of speech signals. Firstly, the scheme of linear feedback shift register combined with inner product to generate measurement matrix is proposed. Secondly, we adopt a new parallel compressive sensing technique to tremendously improve the processing efficiency. Further, the two parties in the communication adopt public key cryptosystem to safely share the key and select a different measurement matrix for each frame of the voice signal to ensure the security. This scheme greatly reduces the difficulty of generating measurement matrix in hardware and improves the processing efficiency. Compared with the existing scheme by Moreno-Alvarado et al., our scheme has reduced the execution time by approximately 8%, and the mean square error (MSE) has also been reduced by approximately 5%....
The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models....
Ambient Assisted Living (AAL) technologies are being developed which could assist elderly people to live healthy and active lives. These technologies have been used to monitor people’s daily exercises, consumption of calories and sleep patterns, and to provide coaching interventions to foster positive behaviour. Speech and audio processing can be used to complement such AAL technologies to inform interventions for healthy ageing by analyzing speech data captured in the user’s home. However, collection of data in home settings presents challenges. One of the most pressing challenges concerns how to manage privacy and data protection. To address this issue, we proposed a low cost system for recording disguised speech signals which can protect user identity by using pitch shifting. The disguised speech so recorded can then be used for training machine learning models for affective behaviour monitoring. Affective behaviour could provide an indicator of the onset of mental health issues such as depression and cognitive impairment, and help develop clinical tools for automatically detecting and monitoring disease progression. In this article, acoustic features extracted from the non-disguised and disguised speech are evaluated in an affect recognition task using six different machine learning classification methods. The results of transfer learning from non-disguised to disguised speech are also demonstrated. We have identified sets of acoustic features which are not affected by the pitch shifting algorithm and also evaluated them in affect recognition. We found that, while the non-disguised speech signal gives the best Unweighted Average Recall (UAR) of 80.01%, the disguised speech signal only causes a slight degradation of performance, reaching 76.29%. The transfer learning from non-disguised to disguised speech results in a reduction of UAR (65.13%). However, feature selection improves the UAR (68.32%). This approach forms part of a large project which includes health and wellbeing monitoring and coaching....
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