BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. RESULTS: Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. CONCLUSIONS: Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

In this paper, we propose an effective method for predicting initial/final duration for corpus-based singing voice synthesis of Mandarin Chinese. The goal of the method is to improve the naturalness and clarity of the synthesized singing voices. To achieve this goal, we construct an individual initial/final (I/F) duration prediction model for each category of consonants. Support vector machine is used for duration prediction in each model. In order to achieve better accuracy, we use both linguistic/phonetic attributes and music-score information as the input features for the I/F duration prediction model. Experimental results demonstrate that the proposed method is effective in predicting the I/F duration for singing voice synthesis.

This paper proposes the concept of a score predictive model (SPM) that can refine the phoneme boundaries obtained by a hidden Markov model (HMM) and dynamic time warping (DTW) for a Mandarin singing voice corpus. An SPM is constructed by using support vector regression. It predicts the score of a phoneme boundary according to the boundary's 58-dimensional feature vector. The correctly identified boundaries of a singing corpus can then be used for corpus-based singing voice synthesis. Several experiments with different settings, including the use of different initial estimates, different acoustic features, and various regression approaches, were designed to verify the feasibility of the proposed approach. Experimental results demonstrate that the proposed SPM is able to effectively refine the results of the HMM and DTW.

This paper proposes a score predictive model (SPM) based approach to integrate two segmentation results obtained by HMM and DTW for a Mandarin singing voice corpus. The SPM can predict the score of a boundary according to its corresponding 14 dimensional feature vector. In order to verify the performance of the proposed method, several experiments were performed. The experimental results demonstrate the feasibility of the proposed approach. Index Terms: automatic phonetic segmentation, boundary refinement, score predictive model

In this paper, we propose a hybrid approach to refine the phonetic boundaries in a Mandarin speech corpus. This approach employs different sets of acoustic features for different categories of phonetic transitions, except for the most difficult case of “periodic voiced + periodic voiced”, which is therefore handled by a heuristic scheme. Several experiments are designed to demonstrate the feasibility of the proposed approach.

In this paper, the design and implementation of a corpus-based singing voice synthesis (SVS) system for Mandarin Chinese was introduced. The design rules of three corpora for singing voice synthesis were proposed. After that, two distance functions were defined and the Viterbi search algorithm was applied to identify the optimal combinations of synthesis units from the three corpora. For better performance, several sound effects with synthesized outputs were combined. Finally, we conduct a listening experiment to demonstrate the feasibility of this system.

Abstract. This paper describes a robust two-phase pitch marking method based on peak-valley decision and dynamic programming. In the first phase, we select either peaks or valleys for pitch mark candidates according to its similarity to an estimated pitch curve. In the second phase, we define state and transition probabilities, and then employ dynamic programming to find the most likely pitch marks. We have also designed different tests to demonstrate the feasibility of the proposed approach. 1

Abstract This article describes the research and development of an efficient Music Information Retrieval (MIR) engine that is embedded in a karaoke software package targeted for Asian people's need of music retrieval. The MIR engine has a multi‐modal interface that allows queries by singing, humming, tapping, speaking, and writing. In particular, we discuss the design philosophy, technical barriers, and performance evaluation of such an engine, as well as its current and potential commercial applications. Feedbacks and feature requests from users, which greatly influence our future work, are also addressed.

New refinement schemes for voice conversion are proposed in this paper. We take mel-frequency cepstral coefficients (MFCC) as the basic feature and adopt cepstral mean subtraction to compensate the channel effects. We propose S/U/V (silence/unvoiced/voiced) decision rule such that two sets of codebooks are used to capture the difference between unvoiced and voiced segments of the source speaker. Moreover, we apply three schemes to refine the synthesized voice, including pitch refinement with PSOLA, energy equalization, and frame concatenation based on synchronized pitch marks. The satisfactory performance of the voice conversion system can be demonstrated through ABX listening test and MOS grade.

This paper proposes a new approach to automatic singing voice rectification. There are two components in the rectifier; one is the recognizer based on dynamic time warping and the other is the synthesizer based PSOLA (Pitch Synchronous Overlap and Add) for pitch shifting. The purpose of the recognizer is to identify the locations of off-key parts of the user's acoustic input. Then with the target music score, the synthesizer tries to correct the off-key parts by appropriate pitch shifting to match the give music score. We also attempt some singing and listening experiments for evaluating the feasibility of the rectifier and the results exhibit the satisfactory performance.