Supervised and Unsupervised Learning of Audio Representations for Music Understanding

In this work, we provide a broad comparative analysis of strategies for pre-training audio understanding models for several tasks in the music domain, including labelling of genre, era, origin, mood, instrumentation, key, pitch, vocal characteristics, tempo and sonority. Specifically, we explore how the domain of pre-training datasets (music or generic audio) and the pre-training methodology (supervised or unsupervised) affects the adequacy of the resulting audio embeddings for downstream tasks. We show that models trained via supervised learning on large-scale expert-annotated music datasets achieve state-of-the-art performance in a wide range of music labelling tasks, each with novel content and vocabularies. This can be done in an efficient manner with models containing less than 100 million parameters that require no fine-tuning or reparameterization for downstream tasks, making this approach practical for industry-scale audio catalogs. Within the class of unsupervised learning strategies, we show that the domain of the training dataset can significantly impact the performance of representations learned by the model. We find that restricting the domain of the pre-training dataset to music allows for training with smaller batch sizes while achieving state-of-the-art in unsupervised learning -- and in some cases, supervised learning -- for music understanding. We also corroborate that, while achieving state-of-the-art performance on many tasks, supervised learning can cause models to specialize to the supervised information provided, somewhat compromising a model's generality

Melody Transcription From Music Audio: Approaches and Evaluation

Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. We discuss melody--roughly, the part a listener might whistle or hum--as one such reduced descriptor of music audio, and consider how to define it, and what use it might be. We go on to describe the results of full-scale evaluations of melody transcription systems conducted in 2004 and 2005, including an overview of the systems submitted, details of how the evaluations were conducted, and a discussion of the results. For our definition of melody, current systems can achieve around 70% correct transcription at the frame level, including distinguishing between the presence or absence of the melody. Melodies transcribed at this level are readily recognizable, and show promise for practical applications

Structured audio content analysis and metadata in a digital library

This work illustrates how audio content analysis of music and manually assigned structural temporal metadata can be used to form a digital library designed for musicological exploration. In addition to text-based searching and browsing, the document view is enriched with an interactive structured audio time-line that shows ground-truth data representing the logical segments to the song, and a version that was automatically generated for comparison. A self-similarity "heat" map is also displayed, and is interactive. Clicking within the map at a co-ordinate (x,y) results in the audio being played simultaneous at time offset x and y, panned left and right, respectively, to make it easier for the listener to separate out the differences. The musicologist can also initiate an audio content based query starting at any point in the song. This produces a ranked result set which can be further studied through their respective document views. Alternatively they can perform a musical structure search (for example, for songs that contain the structure b, b, c, b, c)