This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses Approximate Karbunen–Loeve transform for noise reduction. The proposed QBS-CBMR system uses a music clip as a search key. First, a 51-dimensional matrix containing 39-Mel-frequency cepstral coefficients (MFCCs) features and 12-Chroma features are extracted from an input music clip. Next, adapted symbolic aggregate approximation (adapted SAX) is used to transform each dimension of features into a symbolic sequence. Each symbolic sequence corresponding to each dimension of MFCCs is then converted into a structure called advanced fast pattern index (AFPI) tree. The similarity between the query music clip and the songs in the database is evaluated by calculating a partial score for each AFPI tree. The final score is obtained by calculating the weighted sum of all partial scores, where the weighting of each partial score is determined by its entropy. Experimental results show that the proposed music retrieval system performs robustly and accurately with the entropy weighting mechanism.

This paper presents an ensemble-based speaker recognition using unsupervised data selection. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. A speech utterance is divided into several subsets based on its acoustic characteristics using unsupervised data selection methods. The ensemble classifiers are then trained with these non-overlapping subsets of speech data to improve the recognition accuracy. This new approach has two advantages. First, without any auxiliary information, we use ensemble classifiers based on unsupervised data selection to make use of different acoustic characteristics of speech data. Second, in ensemble classifiers, we apply the divide-and-conquer strategy to avoid a local optimization in the training of a single classifier. Our experiments on the 2010 and 2008 NIST Speaker Recognition Evaluation datasets show that using ensemble classifiers yields a significant performance gain.

Low-cost, compact, and accurate systems for in-home rehabilitation are needed in aging, aged, and hyper-aged groups. In this study, we developed an in-home rehabilitation system for patients with balance disorders by providing visual feedback of postural information in real-time. Our system measures the user's whole body motion and the center of pressure (COP) using a Kinect and Wii Balance Board (WBB). The accuracy of body motion for estimating the anterior folding and lateral bending angles was validated experimentally by comparing the estimates with the angles given by an optical motion capture system. Additional experiments showed that the COP has a small correlation coefficient with the angles, suggesting that WBB is necessary for measuring the COP.