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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)
Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval. It enables music search by instrument, helps recognize musical genres, or can make music transcription easier and more accurate. In this paper, we present a convolutional neural network framework for predominant(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)
This paper presents a novel informed audio source separation algorithm given a limited binary time-frequency annotation. Assuming that all the sources can be represented using a low-rank model, we derive an objective function to minimize the rank of the source spectrogram, and the error between the target and the estimated coefficients. Especially, we apply(More)
A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic analysis is first performed on symbolic data to generate label files. In paral-lel, we synthesize audio data from the same symbolic data,(More)