493 research outputs found
Singing voice correction using canonical time warping
Expressive singing voice correction is an appealing but challenging problem.
A robust time-warping algorithm which synchronizes two singing recordings can
provide a promising solution. We thereby propose to address the problem by
canonical time warping (CTW) which aligns amateur singing recordings to
professional ones. A new pitch contour is generated given the alignment
information, and a pitch-corrected singing is synthesized back through the
vocoder. The objective evaluation shows that CTW is robust against
pitch-shifting and time-stretching effects, and the subjective test
demonstrates that CTW prevails the other methods including DTW and the
commercial auto-tuning software. Finally, we demonstrate the applicability of
the proposed method in a practical, real-world scenario
From heuristics-based to data-driven audio melody extraction
The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications
Melodic Transcription of Flamenco Singing from Monophonic and Polyphonic Music Recordings
We propose a method for the automatic transcription of flamenco singing from monophonic and
polyphonic music recordings. Our transcription system is based on estimating the fundamental frequency (f0)
of the singing voice, and follows an iterative strategy for note segmentation and labelling. The generated
transcriptions are used in the context of melodic similarity, style classification and pattern detection. In our
study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained
transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main
limitations of our approach and discuss the challenges for future studies
Interactive Manipulation of Musical Melody in Audio Recordings
The objective of this project is to develop an interactive technique to manipulate melody in musical recordings. The proposed methodology is based on the use of melody detection methods combined with the invertible constant Q transform (CQT), which allows a high-quality modification of musical content. This work will consist of several stages, the first of which will focus on monophonic recordings and subsequently we will explore methods to manipulate polyphonic recordings. The long-term objective is to alter a melody of a piece of music in such a way that it may sound similar to another. We have set, as and end goal, to allows users to perform melody manipulation and experiment with their music collection. To achieve this goal, we will devise approaches for high quality polyphonic melody manipulation, using a dataset of melodic content and mixed audio recordings. To ensure the system's usability, a listening test or user-study evaluation of the algorithm will be performed
Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music
The extraction of pitch information is arguably one of the most important
tasks in automatic music description systems. However, previous
research and evaluation datasets dealing with pitch estimation focused
on relatively limited kinds of musical data. This work aims to broaden
this scope by addressing symphonic western classical music recordings,
focusing on pitch estimation for melody extraction. This material is characterised
by a high number of overlapping sources, and by the fact that the
melody may be played by different instrumental sections, often alternating
within an excerpt. We evaluate the performance of eleven state-of-the-art
pitch salience functions, multipitch estimation and melody extraction algorithms
when determining the sequence of pitches corresponding to the
main melody in a varied set of pieces. An important contribution of the
present study is the proposed evaluation framework, including the annotation
methodology, generated dataset and evaluation metrics. The results
show that the assumptions made by certain methods hold better than
others when dealing with this type of music signals, leading to a better
performance. Additionally, we propose a simple method for combining
the output of several algorithms, with promising results
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