Incremental Dataset Definition for Large Scale Musicological Research

Abstract

Conducting experiments on large scale musical datasets often requires the definition of a dataset as a first step in the analysis process. This is a classification task, but metadata providing the relevant information is not always available or reliable and manual annotation can be prohibitively expensive. In this study we aim to automate the annotation process using a machine learning approach for classification. We evaluate the effectiveness and the trade-off between accuracy and required number of annotated samples. We present an interactive incremental method based on active learning with uncertainty sampling. The music is represented by features extracted from audio and textual metadata and we evaluate logistic regression, support vector machines and Bayesian classification. Labelled training examples can be iteratively produced with a web-based interface, selecting the samples with lowest classification confidence in each iteration.We apply our method to address the problem of instrumentation identification, a particular case of dataset definition, which is a critical first step in a variety of experiments and potentially also plays a significant role in the curation of digital audio collections. We have used the CHARM dataset to evaluate the effectiveness of our method and focused on a particular case of instrumentation recognition, namely on the detection of piano solo pieces. We found that uncertainty sampling led to quick improvement of the classification, which converged after ca. 100 samples to values above 98%. In our test the textual metadata yield better results * Dan Tidhar is also a member of the Department of Music at City University London. † Edouard Dumon is also a member of ENSTA Paristech. than our audio features and results depend on the learning methods. The results show that effective training of a classifier is possible with our method which greatly reduces the effort of labelling where a residual error rate is acceptable.

A large TV dataset for speech and music activity detection

“…The statistics of these datasets are listed in Table 1. The OpenBMAT [6] and ORF TV dataset [44] are collected from TV programs, and AVASpeech [10] comprises audio from YouTube, while Muspeak [14] has a variety of content such as concert, radio broadcast, and low-fidelity folk music.…”

“…Note that % of music/speech is estimated based on the duration labeled as music or speech and the total duration of the audio content means they can be used only for either speech or music detection, but not both. The GTZAN Speech and Music dataset [11], Scheirer & Slaney Music Speech [12], MUSAN [13], and Muspeak [14] datasets contain only short segments and non-overlapping speech or music labels. Thus, these datasets can only be used for a simplified music and speech segmentation task, where the audio segments can only be classified into either speech, music, or noise without any overlap.…”

Teachers' Data Literacy Skills for Pedagogical Decision Making: Needs Analysis in Lithuania and Germany

“…-Adaptive learning: adaptive learning systems can continuously collect and interpret student data, change the direction and environment of students' learning, taking into account their needs and abilities [16].…”