Refining a Phase Vocoder for Vocal Modulation

Vocal harmonies are a highly sought-after effect in the music industry, as they allow singers to portray more emotion and meaning through their voices. The chords one hears when listening to nearly any modern song are constructed through common ratios of frequencies (e.g., the recipe for a major triad is 4:5:6). Currently, vocal melodies are only readily obtainable through a few methods, including backup singers, looper-effects systems, and post-process overdubbing. The issue with these is that there is currently no publicly-available code that allows solo-artists to modulate input audio to whatever chord structure is desired while maintaining the same duration and timbre in the successive layers. This thesis plans to address this issue using the phase vocoder method. If this modulation technique is successful, this could revolutionize the way vocalists perform. The introduction of real-time self harmonization would allow artists to have access to emphasized lyrical phrases and vocals without needing to hire and train backup vocalists. This phase vocoder would also allow for more vocal improvisation, as the individual would only need to know how to harmonize with themselves and would thus not be relying on interpreting how backup vocalists plan on moving the melody when creating more spontaneously

The nature of applied voice teaching expertise: common elements observed in the lessons of three exemplary applied voice instructors

Based on theories of expert pedagogy, the purpose of this study was to better understand the nature of expertise in voice instruction by observing common elements across three expert voice teachers (Joseph Frank, tenor; Eric Howe, baritone; and Erie Mills, soprano) and discovering the extent voice teaching aligned with common elements of instrumental instruction (Duke & Simmons, 2006a). This qualitative study addressed the overarching question: How can expert voice teaching be characterized? More specifically, using Duke and Simmons’ study of instrumental music teaching expertise as a point of departure, I wanted to understand: To what extent does voice teaching observed in the present study align with Duke and Simmons’ 19 Common Elements of Expertise? Methods of data collection included observation-digital recording of nearly 20 hours of lessons, participant interviews, and field notes. Recorded lessons were reviewed to identify teaching behaviors-attributes that related to students’ goal achievement within “rehearsal frames” (Duke, 1999/2000; 2008). Data analysis occurred in two phases, first coding transcribed data for original elements and second for new elements. Narrative descriptions were created for new elements. Findings gave evidence that expert voice teaching was similar to 14 original elements and revealed nine new elements under three categories: working with a largely invisible and fully embodied instrument, frequent exclusive focus on technique, and drawing on extensive familiarity with texts used for singing. Conclusions advance a theoretical model of voice teaching expertise, drawing on Berliner (1986; 1988). That model has implications for the preparation of novice voice teachers and for further research on voice teaching expertise

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

Music and Lyrics Interactions and their Influence on Recognition of Sung Words: An Investigation of Word Frequency, Rhyme, Metric Stress, Vocal Timbre, Melisma, and Repetition Priming

This study investigated several factors presumed to influence the intelligibility of song lyrics. Twenty-seven participants listened to recordings of musical passages sung in English; each passage consisted of a brief musical phrase sung by a solo voice. Six vocalists produced the corpus of sung phrases. Eight hypotheses derived from common phonological and prosodic principles were tested. Intelligibility of lyrics was degraded: (i) when archaic language was used; (ii) when words were set in melismatic rather than syllabic contexts; (iii) when the musical rhythm did not match the prosodic speech rhythm; and (iv) when successive target words rhymed. Intelligibility of lyrics was facilitated: (i) when words contained diphthongs rather than monophthongs; (ii) when a word from an immediately previous passage reappeared; (iii) when a syllabic setting of a word was preceded by a melismatic setting of the same word. No difference in word intelligibility was observed between music theater singers and opera singers.</jats:p

PoLyScriber: Integrated Training of Extractor and Lyrics Transcriber for Polyphonic Music

Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility. Typically, lyrics transcription can be performed by a two step pipeline, i.e. singing vocal extraction frontend, followed by a lyrics transcriber backend, where the frontend and backend are trained separately. Such a two step pipeline suffers from both imperfect vocal extraction and mismatch between frontend and backend. In this work, we propose a novel end-to-end integrated training framework, that we call PoLyScriber, to globally optimize the vocal extractor front-end and lyrics transcriber backend for lyrics transcription in polyphonic music. The experimental results show that our proposed integrated training model achieves substantial improvements over the existing approaches on publicly available test datasets.Comment: 13 page