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

Songs Search Using Human Humming Voice

The system is developed to find songs stored in the database using human humming voice, whereby a sample of the humming voice is compared to songs stored in the system. The main function of the system is to find songs only by humming to the melody of the song. The scopes for this project are human humming voice, voice capture in WA V format, songs database, and MIDI file comparing algorithm. Methodologies used in this system are based on system analysis and design methodology comprising planning, analysis, design and implementation. Java programming language is used to build the system. The system has the functionality of humming voice recording and algorithms comparing both humming voice and song files in the system to fmd the right song. The intended result of this system is to display the titles of the song and similarity percentage between humming voice melody and songs in the system

Android app for automatic music composition

In this contribution, we present an Android App that generates music automatically replicating a specific contemporary genre, specifically rock music. The composition system is based on the analysis of a set of songs of the desired genre. The analysis results the automatic composition algorithm requires are exported to a JSON file that can be readily imported and parsed. The Android App developed creates a symbolic representation of the music generated for drums, bass and rhythm guitar that can be exported to a MIDI file or audio waveform. The evaluation results show that the automatic compositions generated are not distinguished amongst other excerpts created by human composers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Ministerio de Economía y Competitividad, Gobierno de España, Proyecto TIN2016-75866-C3-2-R

The Original Beat: An Electronic Music Production System and Its Design

The barrier to entry in electronic music production is high. It requires expensive, complicated software, extensive knowledge of music theory and experience with sound generation. Digital Audio Workstations (DAWs) are the main tools used to piece together digital sounds and produce a complete song. While these DAWs are great for music professionals, they have a steep learning curve for beginners and they must run native on a user’s computer. For a novice to begin creating music takes much more time, eort, and money than it should. We believe anyone who is interested in creating electronic music deserves a simple way to digitize their ideas and hear results. With this idea in mind, we created a web based, co-creative system to allow beginners and pros alike to easily create electronic digital music. We outline the requirements for such a system and detail the design and architecture. We go through the specifics of the system we implemented covering the front-end, back-end, server, and generation algorithms. Finally, we will review our development time-line, examine the challenges and risks that arose when building our system, and present future improvements