Download Timbral Attributes for Objective Quality Assessment of the Irish Tin Whistle In this paper we extract various timbral attributes for a variety of Irish tin whistles, and use these attributes to form an objective quality assessment of the instruments. This assessment is compared with the subjective experiences of a number of professional musicians. The timbral attributes are drawn from those developed in the Timbre Model [1].
Download Single-Note Ornamentation Transcription for the Irish Tin Whistle 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 played using single-note ornamentation start and release.
Download Efficiently Computable Similarity Measures for Query by Tapping Systems A Query by Tapping system is a database which contains metadata descriptions of songs. The database can be scanned by tapping the melody line’s rhythm of a song requested on a MIDI keyboard or an e-drum. For the processing of queries the system computes the similarity of the query and the content inside the database by applying a similarity measure. Due to the high number of comparison processes in large databases efficiently computable similarity measures are needed. This paper presents two efficiently computable similarity measures which evaluate rhythmic properties of monophonic melodies represented in an MPEG-7 compliant manner. The usage and effectiveness is presented and evaluated with the real time capable Query by Tapping system BeatBank.
Download An Open Source Tool for Semi-Automatic Rhythmic Annotation We present a plugin implementation for the multi-platform WaveSurfer sound editor. Added functionalities are the semi-automatic extraction of beats at diverse levels of the metrical hierarchy as well as uploading and downloading functionalities to a music metadata database. It is built upon existing open source (GPL-licenced) audio processing tools, namely WaveSurfer, BeatRoot and CLAM, in the intent to expand the scope of those softwares. It is therefore also provided as GPL code with the explicit goal that researchers in the audio processing community can freely use and improve it. We provide technical details of the implementation as well as practical use cases. We also motivate the use of rhythmic metadata in Music Information Retrieval scenarios.
Download A Spectral-Filtering Approach to Music Signal Separation The task of separating a mix of several inter-weaving melodies from a mono recording into multiple tracks is attempted by filtering in the spectral domain. The transcribed score is provided in MIDI format a priori. In each time frame a filter is constructed for each instrument in the mix, whose effect is to filter out all harmonics of that instrument from the DFT spectrum. The complication of overlapping harmonics arising from separate notes is discussed and two filter shapes that were found to be fairly successful at separating overlapping harmonics are presented. In comparing the separated audio tracks to the original instrumental parts, signalto-residual ratios (SRR’s) in excess of 20 dB have been achieved. Audio demonstrations are on the internet [1].
Download Audio Processing Using Haskell The software for most today’s applications including signal processing applications is written in imperative languages. Imperative programs are fast because they are designed close to the architecture of the widespread computers, but they don’t match the structure of signal processing very well. In contrast to that, functional programming and especially lazy evaluation perfectly models many common operations on signals. Haskell is a statically typed, lazy functional programming language which allow for a very elegant and concise programming style. We want to sketch how to process signals, how to improve safety by the use of physical units, and how to compose music using this language.
Download A comparison Between Fixed and Multiresolution Analysis for Onset Detection in Musical Signals A study is presented for the use of multiresolution analysis-based onset detection in the complex domain. It shows that using variable time-resolution across frequency bands generates sharper detection functions for higher bands and more accurate detection functions for lower bands. The resulting method improves the localisation of onsets on fixed-resolution schemes, by favouring the increased time precision of higher subbands during the combination of results.
Download Piano Transcription Using Pattern Recognition: Aspects on Parameter Extraction A method for chord recognition for piano transcription has been previously presented by the authors. The method presents some limitations due to errors in parameter extraction carried out during the training process. Parameter extraction of piano notes is not as straightforward as sometimes can be thought. Spectral components detection is necessary but not enough to obtain accurately some note parameters. The inharmonicity coefficient B is one of the parameters that are difficult to evaluate. The obtained value of B is different for every partial used to calculate it, and sometimes, these differences are high. Tuning with respect to tempered scale is another important note parameter. The problems arise when we try to measure the tuning of a note belonging to octaves 0 or 1, because the fundamental is radiated by the soundboard with a very low level and, therefore, it is not captured by the recording microphone and cannot be measured. A method to avoid these drawbacks is presented in this paper, including an explanation of the basis.
Download On Finding Melodic Lines in Audio Recordings The paper presents our approach to the problem of finding melodic line(s) in polyphonic audio recordings. The approach is composed of two different stages, partially rooted in psychoacoustic theories of music perception: the first stage is dedicated to finding regions with strong and stable pitch (melodic fragments), while in the second stage, these fragments are grouped according to their properties (pitch, loudness...) into clusters which represent melodic lines of the piece. Expectation Maximization algorithm is used in both stages to find the dominant pitch in a region, and to train Gaussian Mixture Models that group fragments into melodies. The paper presents the entire process in more detail and provides some initial results.
Download Musical Instrument Identification in Continuous Recordings Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (bassoon, clarinet, flute, guitar, piano, cello and violin), which yields a high recognition rate. A large and very diverse solo database (108 different solos, all by different performers) is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. First we bring classification results using a very extensive collection of features (62 different feature types), and then use our GDE feature selection algorithm to select a smaller feature set with a relatively short computation time, which allows us to perform instrument recognition in solos in real-time, with only a slight decrease in recognition rate. We demonstrate that our real-time solo classifier can also be useful for instrument recognition in duet performances, and improved using simple “source reduction”.