A Dataset of Norwegian Hardanger Fiddle Recordings with Precise Annotation of Note and Beat Onsets

The Hardanger fiddle is a variety of the violin used in the folk music of the western and central part of southern Norway. This paper presents a dataset of several hours of recordings of Hardanger fiddle music, with note annotations of onsets, offsets and pitches, provided by the performers themselves. A subset has also been annotated with beat onset positions by the performer as well as three expert musicians. The complexity of the music genre—polyphonic, highly ornamented and with a very irregular pulsation, among other aspects—motivated the design of a new annotation software adapted to these particular needs. Beat annotation in MIR is typically recorded as positions in seconds, without explicit connection with actual musical events. In the context of music where the rhythm is carried by the melodic instrument alone, a more reliable definition of beat onsets consists in associating them with the onsets of the notes that represent the start of each beat. This latter definition of beat onsets reflects that beats are generated from within the flow of played melodic-rhythmic events, which implies that the spacing of beats may be shifting and irregular. This motivated the design of a new method for beat annotation in Hardanger fiddle music based on a selection of notes in the note annotation. Comparisons between annotators through alignment—integrated in the interface—enable them to eventually correct their annotations or observe alternative valid interpretations of any given excerpt. After dedicating a part of the note annotation dataset to the training of a machine learning model, for the task of assessing both note pitch and onset time, an F1 score of 87% can be reached. The beat annotation dataset demonstrates the necessity of developing new beat trackers adapted to Hardanger fiddle music. The dataset as well as the annotation software is made publicly available

Tracking the “odd” : Meter inference in a culturally diverse music corpus

In this paper, we approach the tasks of beat tracking, downbeat recognition and rhythmic style classification in nonWestern music. Our approach is based on a Bayesian model, which infers tempo, downbeats and rhythmic style, from an audio signal. The model can be automatically adapted to rhythmic styles and time signatures. For evaluation, we compiled and annotated a music corpus consisting of eight rhythmic styles from three cultures, containing a variety of meter types. We demonstrate that by adapting the model to specific styles, we can track beats and downbeats in odd meter types like 9/8 or 7/8 with an accuracy significantly improved over the state of the art. Even if the rhythmic style is not known in advance, a unified model is able to recognize the meter and track the beat with comparable results, providing a novel method for inferring the metrical structure in culturally diverse datasets.QC 20161031</p

Towards the Automatic Analysis of Metric Modulations

PhDThe metrical structure is a fundamental aspect of music, yet its automatic analysis from audio recordings remains one of the great challenges of Music Information Retrieval (MIR) research. This thesis is concerned with addressing the automatic analysis of changes of metrical structure over time, i.e. metric modulations. The evaluation of automatic musical analysis methods is a critical element of the MIR research and is typically performed by comparing the machine-generated estimates with human expert annotations, which are used as a proxy for ground truth. We present here two new datasets of annotations for the evaluation of metrical structure and metric modulation estimation systems. Multiple annotations allowed for the assessment of inter-annotator (dis)agreement, thereby allowing for an evaluation of the reference annotations used to evaluate the automatic systems. The rhythmogram has been identified in previous research as a feature capable of capturing characteristics of rhythmic content of a music recording. We present here a direct evaluation of its ability to characterise the metrical structure and as a result we propose a method to explicitly extract metrical structure descriptors from it. Despite generally good and increasing performance, such rhythm features extraction systems occasionally fail. When unpredictable, the failures are a barrier to usability and development of trust in MIR systems. In a bid to address this issue, we then propose a method to estimate the reliability of rhythm features extraction. Finally, we propose a two-fold method to automatically analyse metric modulations from audio recordings. On the one hand, we propose a method to detect metrical structure changes from the rhythmogram feature in an unsupervised fashion. On the other hand, we propose a metric modulations taxonomy rooted in music theory that relies on metrical structure descriptors that can be automatically estimated. Bringing these elements together lays the ground for the automatic production of a musicological interpretation of metric modulations.EPSRC award 1325200 and Omnifone Ltd