Music Transcription Within Irish Traditional Music

Transcribing Irish traditional music is an open-field of research. The oral transmission of the music between generations explains the lack of transcription until recent times. The music can be played solo, which permits the player to exploit the variety of ornamentation types, in unison, and also with the accompaniment of a harmonic instrument. Different signal processing applications for transcribing Irish traditional music are presented in this thesis, including onset, ornamentation and pitch detection. An onset detection system which focuses on the characteristics of the tin whistle within Irish traditional music is first presented. The tin whistle is a good example of the features of Irish traditional music, and the detection of its onset encounters all the problems associated with onset detection identified in the literature review. An extension of this method is also implemented in an effort to detect the most common types of ornamentation, which has not been attempted to date. Existing onset detectors utilise energy and/or phase information to detect onsets. A novel onset detector, which focuses on the harmonicity of the signal to detect the onsets by using comb filters, is presented. This methods overcomes the difficulties encountered by existing onset detection approaches in respect of signal modulations and detection of slow onsets. Finally, a further comb filter based method is utilised to detect the triads played by a harmonic accompaniment. A set of results is presented for the four methods, followed by a commentary and explanation of the novel contributions

Automating Ornamentation Transcription

A novel technique for detecting single and multi-note ornaments is presented. The system detects audio segments by utilising and onset detector based on comb filters (ODCF), which is capable of detecting very close events. In addition, a novel method to remove spurious onsets due to offset events is introduced. The system utilises musical ornamentation theory to decide whether a sequence of audio segments correspond to an ornamentation musical structure. In order to evaluate the results, a database of signals produced by different players using the three different instruments has been utilised. The results represent a step forward towards fully automating ornamentation transcriptio

Single-note Ornementation Transcription for the Irish Tin Whislte based on Onset Detection

Ornamentation plays a very important role in Irish Traditional music, giving more expression to the music by altering or embellishing small pieces of a melody. Single-note ornamentation, such as cuts and strikes, are the most common type in Irish Traditional music and are played by articulating the note pitch during the onset stage. A technique for transcribing single note ornamentation for the tin whistle based on onset detection is presented. This method focuses on the characteristics of the tin whistle within Irish traditional music, customising a time-frequency based representation for detecting the instant when new notes are played using single-note ornamentation start and release

Single-note Ornementation Transcription for the Irish Tin Whislte based on Onset Detection

Ornamentation plays a very important role in Irish Traditional music, giving more expression to the music by altering or embellishing small pieces of a melody. Single-note ornamentation, such as cuts and strikes, are the most common type in Irish Traditional music and are played by articulating the note pitch during the onset stage. A technique for transcribing single note ornamentation for the tin whistle based on onset detection is presented. This method focuses on the characteristics of the tin whistle within Irish traditional music, customising a time-frequency based representation for detecting the instant when new notes are played using single-note ornamentation start and release

Evaluating Ground Truth for ADRess as a Preprocess for Automatic Musical Instrument Identification

Most research in musical instrument identification has focused on labeling isolated samples or solo phrases. A robust instrument identification system capable of dealing with polytimbral recordings of instruments remains a necessity in music information retrieval. Experiments are described which evaluate the ground truth of ADRess as a sound source separation technique used as a preprocess to automatic musical instrument identification. The ground truth experiments are based on a number of basic acoustic features, while using a Gaussian Mixture Model as the classification algorithm. Using all 44 acoustic feature dimensions, successful identification rates are achieved

Multi pitch estimation by using modified IIR Comb Filters

A technique for detecting the pitches of a polyphonic signal of presented. The system utilises modified IIR comb filters, which are generated to ensure that n null (stop band notches) exists at multiples of note frequencies, and that a very flat pass band is present in the remain of the spectrum. Thus, the signal spectrum is not distorted after applying the filters 60 the audio signal, which is the case when using FIR comb filters. The presented approach improves upon an existing multi pitch detection model bared on an FIR comb filter framework

Harmonic Sound Source Separation using FIR Comb Filters

A technique for separating harmonic sound sources using FIR comb filters is presented. First, a pre-processing task is performed by a multipitch estimator to detect the pitches that the signal is composed of. Then, a method based on the Short Time Fourier Transform (STFT) is utilized to iteratively extract the harmonics belonging to a given source by using FIR comb filters. The presented approach improves upon existing sinusoidal model approaches in terms of the perceptual quality of the extracted signal

Linear Prediction: The Problem, its Solution and Application to Speech

Linear prediction is a signal processing technique that is used extensively in the analysis of speech signals and, as it is so heavily referred to in speech processing literature, a certain level of familiarity with the topic is typically required by all speech processing engineers. This paper aims to provide a well-rounded introduction to linear prediction, and so doing, facilitate the understanding of the technique. Linear prediction and its mathematical derivation will be described, with a specific focus on applying the technique to speech signals. It is noted, however, that although progress in linear prediction has been driven primarily by speech research, it involves concepts that prove useful to digital signal processing in general

Automating Ornamentation Transcription

A novel technique for detecting single and multi-note ornaments is presented. The system detects audio segments by utilising and onset detector based on comb filters (ODCF), which is capable of detecting very close events. In addition, a novel method to remove spurious onsets due to offset events is introduced. The system utilises musical ornamentation theory to decide whether a sequence of audio segments correspond to an ornamentation musical structure. In order to evaluate the results, a database of signals produced by different players using the three different instruments has been utilised. The results represent a step forward towards fully automating ornamentation transcriptio

Pitch Tracking and Voiced/Unvoiced Detection in Noisy Environment using Optimat Sequence Estimation

This paper addresses the problem of pitch tracking and voiced/unvoiced detection in noisy speech environments. An algorithm is presented which uses a number of variable thresholds to track pitch contour with minimal error. This is achieved by modeling the pitch tracking problem in such a way that allows the use of optimal estimation methods, such MLSE. The performance of the algorithm is evaluated using the Keele pitch detection database with realistic background noise. Results show best performance in comparison to other state of the art pitch detector and successful pitch tracking is possible in low signal to noise conditions