Vocal Detection: An evaluation between general versus focused models

This thesis focuses on presenting a technique on improving current vocal detection methods. One of the most popular methods employs some type of statistical approach where vocal signals can be distinguished automatically by first training a model on both vocal and non-vocal example data, then using this model to classify audio signals into vocals or non-vocals. There is one problem with this method which is that the model that has been trained is typically very general and does its best at classifying various different types of data. Since the audio signals containing vocals that we care about are songs, we propose to improve vocal detection accuracies by creating focused models targeted at predicting vocal segments according to song artist and artist gender. Such useful information like artist name are often overlooked, this restricts opportunities in processing songs more specific to its type and hinders its potential success. Experiment results with several models built according to artist and artist gender reveal improvements of up to 17% when compared to using the general approach. With such improvements, applications such as automatic lyric synchronization to vocal segments in real-time may become more achievable with greater accuracy

Mandarin Singing Voice Synthesis Based on Harmonic Plus Noise Model and Singing Expression Analysis

The purpose of this study is to investigate how humans interpret musical scores expressively, and then design machines that sing like humans. We consider six factors that have a strong influence on the expression of human singing. The factors are related to the acoustic, phonetic, and musical features of a real singing signal. Given real singing voices recorded following the MIDI scores and lyrics, our analysis module can extract the expression parameters from the real singing signals semi-automatically. The expression parameters are used to control the singing voice synthesis (SVS) system for Mandarin Chinese, which is based on the harmonic plus noise model (HNM). The results of perceptual experiments show that integrating the expression factors into the SVS system yields a notable improvement in perceptual naturalness, clearness, and expressiveness. By one-to-one mapping of the real singing signal and expression controls to the synthesizer, our SVS system can simulate the interpretation of a real singer with the timbre of a speaker.Comment: 8 pages, technical repor

VOCAL SEGMENT CLASSIFICATION IN POPULAR MUSIC

This paper explores the vocal and non-vocal music classification problem within popular songs. A newly built labeled database covering 147 popular songs is announced. It is designed for classifying signals from 1sec time windows. Features are selected for this particular task, in order to capture both the temporal correlations and the dependencies among the feature dimensions. We systematically study the performance of a set of classifiers, including linear regression, generalized linear model, Gaussian mixture model, reduced kernel orthonormalized partial least squares and K-means on cross-validated training and test setup. The database is divided in two different ways: with/without artist overlap between training and test sets, so as to study the so called ‘artist effect’. The performance and results are analyzed in depth: from error rates to sample-to-sample error correlation. A voting scheme is proposed to enhance the performance under certain conditions

From raw audio to a seamless mix : creating an automated DJ system for drum and bass

We present the open-source implementation of the first fully automatic and comprehensive DJ system, able to generate seamless music mixes using songs from a given library much like a human DJ does. The proposed system is built on top of several enhanced music information retrieval (MIR) techniques, such as for beat tracking, downbeat tracking, and structural segmentation, to obtain an understanding of the musical structure. Leveraging the understanding of the music tracks offered by these state-of-the-art MIR techniques, the proposed system surpasses existing automatic DJ systems both in accuracy and completeness. To the best of our knowledge, it is the first fully integrated solution that takes all basic Wing best practices into account, from beat and downbeat matching to identification of suitable cue points, determining a suitable cross-fade profile and compiling an interesting playlist that trades off innovation with continuity. To make this possible, we focused on one specific sub-genre of electronic dance music, namely Drum and Bass. This allowed us to exploit genre-specific properties, resulting in a more robust performance and tailored mixing behavior. Evaluation on a corpus of 160 Drum and Bass songs and an additional hold-out set of 220 songs shows that the used MIR algorithms can annotate 91% of the songs with fully correct annotations (tempo, beats, downbeats, and structure for cue points). On these songs, the proposed song selection process and the implemented Wing techniques enable the system to generate mixes of high quality, as confirmed by a subjective user test in which 18 Drum and Bass fans participated

Lyrics-to-Audio Alignment and its Application

Automatic lyrics-to-audio alignment techniques have been drawing attention in the last years and various studies have been made in this field. The objective of lyrics-to-audio alignment is to estimate a temporal relationship between lyrics and musical audio signals and can be applied to various applications such as Karaoke-style lyrics display. In this contribution, we provide an overview of recent development in this research topic, where we put a particular focus on categorization of various methods and on applications