Large-scale cover song identification using chord profiles

This paper focuses on cover song identification among datasets potentially containing millions of songs. A compact representation of music contents plays an important role in large-scale analysis and retrieval. The proposed approach is based on high-level summarization of musical songs using chord profiles. Search is performed in two steps. In the first step, the Locality Sensitive Hashing (LHS) method is used to retrieve songs with similar chord profiles. On the resulting list of songs a second processing step is applied to progressively refine the ranking. Experiments conducted on both the Million Song Dataset (MSD) and a subset of the Second Hand Songs (SHS) dataset showed the effectiveness of the proposed solution, which provides state-of-the-art results

Time-frequency reassigned features for automatic chord recognition

This paper addresses feature extraction for automatic chord recognition systems. Most chord recognition systems use chroma features as a front-end and some kind of classifier (HMM, SVM or template matching). The vast majority of feature extraction approaches are based on mapping frequency bins from spectrum or constant-Q spectrum to chroma bins. In this work a set of new chroma features that are based on the time-frequency reassignment (TFR) technique is investigated. The proposed feature set was evaluated on the commonly used Beatles dataset and proved to be efficient for the chord recognition task, outperforming standard chroma

A probabilistic approach to simultaneous extraction of beats and downbeats

This paper focuses on the automatic extraction of beat structure from a musical piece. A novel statistical approach to modeling beat sequences based on the application of Hidden Markov Models (HMM) is introduced. The resulting beat labels are obtained by running the Viterbi decoder and subsequent lattice rescoring. For the observation vectors we propose a new feature set that is based on the impulsive and harmonic components of the reassigned spectrogram. Different components of observation vectors have been investigated for their efficiency. The main advantage of the proposed approach is the absence of imposed deterministic rules. All the parameters are learned from the training data, and the experimental results show the efficiency of the proposed schema