In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with binary decision trees SVM (BDTSVM) architecture.
![1focus windows 1focus windows](https://appletoolbox.com/wp-content/uploads/2019/07/BlockedWebsiteUsingTerminalMac-540x318.jpg)
The final form of proposed BDASVM is created by combining four BDSVM discriminators supplemented by decision table. The overall classification accuracy is conditioned by finding the optimal parameters for discrimination function resulting in higher computational complexity. The fundamental element of BDASVM is the binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes utilizing decision function modeled by separating hyperplane. Therefore, we developed a binary discrimination architecture employing the SVM classifier (BDASVM) with intention to use it for classification of broadcast news (BN) audio data. We aimed our effort towards building the classification architecture employing the combination of multiple binary SVM (Support Vector Machine) classifiers for solving two-class discrimination problem. The main idea of proposed solution is derived from the fact that solving one binary discrimination problem multiple times can reduce the overall miss-classification error. The classification has been accomplished using both artificial neural networks and support vector machines and according to the results the proposed feature set outperforms the traditional onesĪ multi-level classification architecture for solving binary discrimination problem is proposed in this paper. The principal component analysis has been applied to eliminate the correlated features. The best performance is obtained with Daubechies-8 wavelet among the other members of the Daubechies family, considering the number of vanishing moments and orthogonality. A database which contains a wide variety of radio recordings from internet radios with different male and female speakers and various genres of musical pieces is constructed. Due to the good representation ability of the wavelets, a high accuracy classification can be obtained even for a short window of 0,5 seconds. The feature set is constructed using the mean and variances of discrete wavelet coefficients and ratio of the change between the wavelet subbands. In this study, a discrete wavelet transform based feature set has been proposed for discrimination of music and speech. The results indicate that, while the proposed system protects the speech successfully in the presence of speech, its noise suppression performance is better than the other tested systems in the absence of speech. In this study, the performance of the proposed system is compared with two different speech protected noise cancellation systems in the presence and absence of speech, at different SNRs from low to high, and in two different microphone configurations. The performances of most speech protected noise cancellation systems in the literature have been calculated at high signal-to-noise ratios (SNRs) that are unrealistic for working environments and only in the presence of speech situations. In the VAD block, to find the feature that best detects speech in a noisy environment, different feature extraction methods are implemented, and also a deep neural network (DNN), that extracts features from raw data is employed. In this paper, a speech protected noise cancellation system consisting of dual voice activity detection (VAD), convolutive blind source separation (CBSS), and noise cancellation blocks, is presented. Thus, HPDs need speech protected noise cancellation systems that isolate and enhance speech while reducing harmful background noise. Since traditional HPDs suppress all sounds in the environment, users are exposed to dangers as they are unaware of verbal instructions and warnings.
![1focus windows 1focus windows](https://images.idgesg.net/images/article/2020/03/focus-assist-100836576-orig.jpg)
People working in noisy environments should wear hearing protective devices (HPD) to prevent noise-induced hearing loss.