Learn More
—We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extremely laborious task of human annotation of chord names and boundaries—which must be done to provide machine learning models with ground truth—by performing automatic harmony(More)
A new approach for acoustic chord transcription and key extraction is presented. We use a novel method of acquiring a large set of labeled training data for automatic key/chord recognition from the raw audio without the enormously laborious process of manual annotation. To this end, we first perform harmonic analysis on symbolic data to extract the key(More)
—Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a(More)
Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music(More)
The human ability to recognize, identify and compare sounds based on their approximation of particular vowels provides an intuitive, easily learned representation for complex data. We describe implementations of vocal tract models specifically designed for sonification purposes. The models described are based on classical models including Klatt[1] and(More)
In this paper, we propose a novel method for obtaining labeled training data to estimate the parameters in a supervised learning model for automatic chord recognition. To this end, we perform harmonic analysis on symbolic data to generate label files. In parallel, we generate audio data from the same symbolic data, which are then provided to a machine(More)