Wavelet Transformation and Spectral Subtraction Method in Performing Automated Rindik Song Transcription

Rindik is Balinese traditional music consisting of bamboo rods arranged horizontally and played by hitting the rods with a mallet-like tool called "panggul". In this study, the transcription of Rindik's music songs was carried out automatically using the Wavelet transformation method and spectral subtraction. Spectral subtraction method is used with iterative estimation and separation approaches. While the Wavelet transformation method is used by matching the segment Wavelet results with the Wavelet result references in the dataset. The results of the transcription were also synthesized again using the concatenative synthesis method. The data used is the hit of 1 Rindik rod and a combination of 2 Rindik rods that are hit simultaneously, and for testing the system, 4 Rindik songs are used. Each data was recorded 3 times. Several parameters are used for the Wavelet transformation method and spectral subtraction, which are the length of the frame for the Wavelet transformation method and the tolerance interval for frequency difference in spectral subtraction method. The test is done by measuring the accuracy of the transcription from the system within all Rindik song data. As a result, the Wavelet transformation method produces an average accuracy of 83.42% and the spectral subtraction method produces an average accuracy of 78.51% in transcription of Rindik songs

The DESAM toolbox: spectral analysis of musical audio

International audienceIn this paper is presented the DESAM Toolbox, a set of Matlab functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processing tools that decompose music files according to different signal models, giving rise to different ``mid-level'' representations. After motivating the need for such a toolbox, this paper offers an overview of the overall organization of the toolbox, and describes all available functionalities

Towards the automated analysis of simple polyphonic music : a knowledge-based approach

PhDMusic understanding is a process closely related to the knowledge and experience of the listener. The amount of knowledge required is relative to the complexity of the task in hand. This dissertation is concerned with the problem of automatically decomposing musical signals into a score-like representation. It proposes that, as with humans, an automatic system requires knowledge about the signal and its expected behaviour to correctly analyse music. The proposed system uses the blackboard architecture to combine the use of knowledge with data provided by the bottom-up processing of the signal's information. Methods are proposed for the estimation of pitches, onset times and durations of notes in simple polyphonic music. A method for onset detection is presented. It provides an alternative to conventional energy-based algorithms by using phase information. Statistical analysis is used to create a detection function that evaluates the expected behaviour of the signal regarding onsets. Two methods for multi-pitch estimation are introduced. The first concentrates on the grouping of harmonic information in the frequency-domain. Its performance and limitations emphasise the case for the use of high-level knowledge. This knowledge, in the form of the individual waveforms of a single instrument, is used in the second proposed approach. The method is based on a time-domain linear additive model and it presents an alternative to common frequency-domain approaches. Results are presented and discussed for all methods, showing that, if reliably generated, the use of knowledge can significantly improve the quality of the analysis.Joint Information Systems Committee (JISC) in the UK National Science Foundation (N.S.F.) in the United states. Fundacion Gran Mariscal Ayacucho in Venezuela

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Audio source separation techniques including novel time-frequency representation tools

The thesis explores the development of tools for audio representation with applications in Audio Source Separation and in the Music Information Retrieval (MIR) field. A novel constant Q transform was introduced, called IIR-CQT. The transform allows a flexible design and achieves low computational cost. Also, an independent development of the Fan Chirp Transform (FChT) with the focus on the representation of simultaneous sources is studied, which has several applications in the analysis of polyphonic music signals. Dierent applications are explored in the MIR field, some of them directly related with the low-level representation tools that were analyzed. One of these applications is the development of a visualization tool based in the FChT that proved to be useful for musicological analysis . The tool has been made available as an open source, freely available software. The proposed Transform has also been used to detect and track fundamental frequencies of harmonic sources in polyphonic music. Also, the information of the slope of the pitch was used to define a similarity measure between two harmonic components that are close in time. This measure helps to use clustering algorithms to track multiple sources in polyphonic music. Additionally, the FChT was used in the context of the Query by Humming application. One of the main limitations of such application is the construction of a search database. In this work, we propose an algorithm to automatically populate the database of an existing Query by Humming, with promising results. Finally, two audio source separation techniques are studied. The first one is the separation of harmonic signals based on the FChT. The second one is an application for which the fundamental frequency of the sources is assumed to be known (Score Informed Source Separation problem)

End-to-End Music Transcription Using Fine-Tuned Variable-Q Filterbanks

The standard time-frequency representations calculated to serve as features for musical audio may have reached the extent of their effectiveness. General-purpose features such as Mel-Frequency Spectral Coefficients or the Constant-Q Transform, while being pyschoacoustically and musically motivated, may not be optimal for all tasks. As large, comprehensive, and well-annotated musical datasets become increasingly available, the viability of learning from the raw waveform of recordings widens. Deep neural networks have been shown to perform feature extraction and classification jointly. With sufficient data, optimal filters which operate in the time-domain may be learned in place of conventional time-frequency calculations. Since the spectrum of problems studied by the Music Information Retrieval community are vastly different, rather than relying on the fixed frequency support of each bandpass filter within standard transforms, learned time-domain filters may prioritize certain harmonic frequencies and model note behavior differently based on a specific music task. In this work, the time-frequency calculation step of a baseline transcription architecture is replaced with a learned equivalent, initialized with the frequency response of a Variable-Q Transform. The learned replacement is fine-tuned jointly with a baseline architecture for the task of piano transcription, and the resulting filterbanks are visualized and evaluated against the standard transform

Signal Processing Methods for Music Synchronization, Audio Matching, and Source Separation

The field of music information retrieval (MIR) aims at developing techniques and tools for organizing, understanding, and searching multimodal information in large music collections in a robust, efficient and intelligent manner. In this context, this thesis presents novel, content-based methods for music synchronization, audio matching, and source separation. In general, music synchronization denotes a procedure which, for a given position in one representation of a piece of music, determines the corresponding position within another representation. Here, the thesis presents three complementary synchronization approaches, which improve upon previous methods in terms of robustness, reliability, and accuracy. The first approach employs a late-fusion strategy based on multiple, conceptually different alignment techniques to identify those music passages that allow for reliable alignment results. The second approach is based on the idea of employing musical structure analysis methods in the context of synchronization to derive reliable synchronization results even in the presence of structural differences between the versions to be aligned. Finally, the third approach employs several complementary strategies for increasing the accuracy and time resolution of synchronization results. Given a short query audio clip, the goal of audio matching is to automatically retrieve all musically similar excerpts in different versions and arrangements of the same underlying piece of music. In this context, chroma-based audio features are a well-established tool as they possess a high degree of invariance to variations in timbre. This thesis describes a novel procedure for making chroma features even more robust to changes in timbre while keeping their discriminative power. Here, the idea is to identify and discard timbre-related information using techniques inspired by the well-known MFCC features, which are usually employed in speech processing. Given a monaural music recording, the goal of source separation is to extract musically meaningful sound sources corresponding, for example, to a melody, an instrument, or a drum track from the recording. To facilitate this complex task, one can exploit additional information provided by a musical score. Based on this idea, this thesis presents two novel, conceptually different approaches to source separation. Using score information provided by a given MIDI file, the first approach employs a parametric model to describe a given audio recording of a piece of music. The resulting model is then used to extract sound sources as specified by the score. As a computationally less demanding and easier to implement alternative, the second approach employs the additional score information to guide a decomposition based on non-negative matrix factorization (NMF)

Audio source separation for music in low-latency and high-latency scenarios

Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de separació de veu cantada, baix i percussió. En segon lloc, proposem diversos mètodes d'alta latència que milloren la separació de la veu cantada, gràcies al modelatge de components específics, com la respiració i les consonants. Finalment, explorem l'ús de correlacions temporals i anotacions manuals per millorar la separació dels instruments de percussió i dels senyals musicals polifònics complexes.Esta tesis propone métodos para tratar las limitaciones de las técnicas existentes de separación de fuentes musicales en condiciones de baja y alta latencia. En primer lugar, nos centramos en los métodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularización de Tikhonov como método de descomposición del espectro en el contexto de baja latencia. Lo comparamos con las técnicas existentes en tareas de estimación y seguimiento de los tonos, que son pasos cruciales en muchos métodos de separación. A continuación utilizamos y evaluamos el método de descomposición del espectro en tareas de separación de voz cantada, bajo y percusión. En segundo lugar, proponemos varios métodos de alta latencia que mejoran la separación de la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiración y las consonantes. Finalmente, exploramos el uso de correlaciones temporales y anotaciones manuales para mejorar la separación de los instrumentos de percusión y señales musicales polifónicas complejas.This thesis proposes specific methods to address the limitations of current music source separation methods in low-latency and high-latency scenarios. First, we focus on methods with low computational cost and low latency. We propose the use of Tikhonov regularization as a method for spectrum decomposition in the low-latency context. We compare it to existing techniques in pitch estimation and tracking tasks, crucial steps in many separation methods. We then use the proposed spectrum decomposition method in low-latency separation tasks targeting singing voice, bass and drums. Second, we propose several high-latency methods that improve the separation of singing voice by modeling components that are often not accounted for, such as breathiness and consonants. Finally, we explore using temporal correlations and human annotations to enhance the separation of drums and complex polyphonic music signals

Separation of musical sources and structure from single-channel polyphonic recordings

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