198 research outputs found
Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency
accepteddate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfdate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfWe present Tony, a software tool for the interactive an- notation of melodies from monophonic audio recordings, and evaluate its usability and the accuracy of its note extraction method. The scientific study of acoustic performances of melodies, whether sung or played, requires the accurate transcription of notes and pitches. To achieve the desired transcription accuracy for a particular application, researchers manually correct results obtained by automatic methods. Tony is an interactive tool directly aimed at making this correction task efficient. It provides (a) state-of-the art algorithms for pitch and note estimation, (b) visual and auditory feedback for easy error-spotting, (c) an intelligent graphical user interface through which the user can rapidly correct estimation errors, (d) extensive export functions enabling further processing in other applications. We show that Tonyâs built in automatic note transcription method compares favourably with existing tools. We report how long it takes to annotate recordings on a set of 96 solo vocal recordings and study the effect of piece, the number of edits made and the annotatorâs increasing mastery of the software. Tony is Open Source software, with source code and compiled binaries for Windows, Mac OS X and Linux available from https://code.soundsoftware.ac.uk/projects/tony/
Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines
The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing
Application of automatic speech recognition technologies to singing
The research field of Music Information Retrieval is concerned with the automatic analysis of musical characteristics. One aspect that has not received much attention so far is the automatic analysis of sung lyrics. On the other hand, the field of Automatic Speech Recognition has produced many methods for the automatic analysis of speech, but those have rarely been employed for singing. This thesis analyzes the feasibility of applying various speech recognition methods to singing, and suggests adaptations. In addition, the routes to practical applications for these systems are described. Five tasks are considered: Phoneme recognition, language identification, keyword spotting, lyrics-to-audio alignment, and retrieval of lyrics from sung queries. The main bottleneck in almost all of these tasks lies in the recognition of phonemes from sung audio. Conventional models trained on speech do not perform well when applied to singing. Training models on singing is difficult due to a lack of annotated data. This thesis offers two approaches for generating such data sets. For the first one, speech recordings are made more âsong-likeâ. In the second approach, textual lyrics are automatically aligned to an existing singing data set. In both cases, these new data sets are then used for training new acoustic models, offering considerable improvements over models trained on speech. Building on these improved acoustic models, speech recognition algorithms for the individual tasks were adapted to singing by either improving their robustness to the differing characteristics of singing, or by exploiting the specific features of singing performances. Examples of improving robustness include the use of keyword-filler HMMs for keyword spotting, an i-vector approach for language identification, and a method for alignment and lyrics retrieval that allows highly varying durations. Features of singing are utilized in various ways: In an approach for language identification that is well-suited for long recordings; in a method for keyword spotting based on phoneme durations in singing; and in an algorithm for alignment and retrieval that exploits known phoneme confusions in singing.Das Gebiet des Music Information Retrieval befasst sich mit der automatischen Analyse von musikalischen Charakteristika. Ein Aspekt, der bisher kaum erforscht wurde, ist dabei der gesungene Text. Auf der anderen Seite werden in der automatischen Spracherkennung viele Methoden fĂŒr die automatische Analyse von Sprache entwickelt, jedoch selten fĂŒr Gesang. Die vorliegende Arbeit untersucht die Anwendung von Methoden aus der Spracherkennung auf Gesang und beschreibt mögliche Anpassungen. Zudem werden Wege zur praktischen Anwendung dieser AnsĂ€tze aufgezeigt. FĂŒnf Themen werden dabei betrachtet: Phonemerkennung, Sprachenidentifikation, Schlagwortsuche, Text-zu-Gesangs-Alignment und Suche von Texten anhand von gesungenen Anfragen. Das gröĂte Hindernis bei fast allen dieser Themen ist die Erkennung von Phonemen aus Gesangsaufnahmen. Herkömmliche, auf Sprache trainierte Modelle, bieten keine guten Ergebnisse fĂŒr Gesang. Das Trainieren von Modellen auf Gesang ist schwierig, da kaum annotierte Daten verfĂŒgbar sind. Diese Arbeit zeigt zwei AnsĂ€tze auf, um solche Daten zu generieren. FĂŒr den ersten wurden Sprachaufnahmen kĂŒnstlich gesangsĂ€hnlicher gemacht. FĂŒr den zweiten wurden Texte automatisch zu einem vorhandenen Gesangsdatensatz zugeordnet. Die neuen DatensĂ€tze wurden zum Trainieren neuer Modelle genutzt, welche deutliche Verbesserungen gegenĂŒber sprachbasierten Modellen bieten. Auf diesen verbesserten akustischen Modellen aufbauend wurden Algorithmen aus der Spracherkennung fĂŒr die verschiedenen Aufgaben angepasst, entweder durch das Verbessern der Robustheit gegenĂŒber Gesangscharakteristika oder durch das Ausnutzen von hilfreichen Besonderheiten von Gesang. Beispiele fĂŒr die verbesserte Robustheit sind der Einsatz von Keyword-Filler-HMMs fĂŒr die Schlagwortsuche, ein i-Vector-Ansatz fĂŒr die Sprachenidentifikation sowie eine Methode fĂŒr das Alignment und die Textsuche, die stark schwankende Phonemdauern nicht bestraft. Die Besonderheiten von Gesang werden auf verschiedene Weisen genutzt: So z.B. in einem Ansatz fĂŒr die Sprachenidentifikation, der lange Aufnahmen benötigt; in einer Methode fĂŒr die Schlagwortsuche, die bekannte Phonemdauern in Gesang mit einbezieht; und in einem Algorithmus fĂŒr das Alignment und die Textsuche, der bekannte Phonemkonfusionen verwertet
Classification of musical genres using hidden Markov models
The music content online is expanding fast, and music streaming services are in need for algorithms that sort new music. Sorting music by their characteristics often comes down to considering the genre of the music. Numerous studies have been made on automatic classiïŹcation of audio ïŹles using spectral analysis and machine learning methods. However, many of the completed studies have been unrealistic in terms of usefulness in real settings, choosing genres that are very dissimilar. The aim of this masterâs thesis is to try a more realistic scenario, with genres of which the border between them is uncertain, such as Pop and R&B. Mel-frequency cepstral coefïŹcients (MFCCs) were extracted from audio ïŹles and used as a multidimensional Gaussian input to a hidden Markov model (HMM) to classify the four genres Pop, Jazz, Classical and R&B. An alternative method is tested, using a more theoretical approach of music characteristics to improve classiïŹcation. The maximum total accuracy obtained when tested on an external test set was 0.742 for audio data, and 0.540 for theoretical data, implying that a combination of the two methods will not result in an increase of accuracy. Different methods of evaluation and possible alternative approaches are discussed
A Comprehensive Trainable Error Model for Sung Music Queries
We propose a model for errors in sung queries, a variant of the hidden Markov
model (HMM). This is a solution to the problem of identifying the degree of
similarity between a (typically error-laden) sung query and a potential target
in a database of musical works, an important problem in the field of music
information retrieval. Similarity metrics are a critical component of
query-by-humming (QBH) applications which search audio and multimedia databases
for strong matches to oral queries. Our model comprehensively expresses the
types of error or variation between target and query: cumulative and
non-cumulative local errors, transposition, tempo and tempo changes,
insertions, deletions and modulation. The model is not only expressive, but
automatically trainable, or able to learn and generalize from query examples.
We present results of simulations, designed to assess the discriminatory
potential of the model, and tests with real sung queries, to demonstrate
relevance to real-world applications
MILITARY COMMUNICATIONS AND INFORMATION SYSTEMS CONFERENCE Is speech technology ready for use now?
Research and development in speech technology has been performed for almost 30 years now. Coming from experimental systems, a set of products has been developed in this time. From the view of the potential users, the main question remains: has the technology reached a state where it can be used meaningfully? This paper will discuss this question- and will give an overview of the tasks that speech recognition can solve and the state for usability for each of the task
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