We present a novel approach to matching and recognition of 2D shapes, which uses a chord-based shape representation and matches shapes by finding correspondences between chords. Given a 2D shape, we choose its basis chord such that both end points of the chord have high probability of being present under occlusion. Then the shape is transformed and represented in an internal reference frame uni...

We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harmonic, with only few dissonant notes. It has clear long-term structure that is...

Numerous earlier studies have investigated the cognitive processing of musical syntax with regular and irregular chord sequences. However, irregular sequences may also be perceived as unexpected, and therefore have a different emotional valence than regular sequences. We provide behavioral data showing that irregular chord functions presented in chord sequence paradigms are perceived as less pl...

This paper presents the implementation of the “Self-Chord” P2P system. Self-Chord inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomy behavior, self-organization and capacity to adapt to a changing environment. Self-Chord has three main adv...

This paper focuses on automatic melody transcription in a situation where a chord transcription is already available. Given an excerpt of music in audio form and a chord transcription in symbolic form, the task is to create a symbolic melody transcription that consists of note onset times and pitches. We present an algorithm that divides the audio into segments based on the chord transcription,...

This paper describes a system that maps a musical performance, recorded as MIDI, onto a partially specified score (lead sheet). Our system breaks the performance into appropriate segments for hypothesized chords by representing it as a directed acyclic graph (DAG) whose edges represent time-intervals of the music. The highest-reward path through the DAG corresponds to the best segmentation of t...

The most significant problem faced by Machine Learningbased chord recognition systems is arguably the lack of highquality training examples. In this paper, we address this problem by leveraging the availability of chord annotations from guitarist websites. We show that such annotations can be used as partial supervision of a semi-supervised chord recognition method—partial since accurate timing...

The present study investigated whether neural responses could demonstrate the statistical learning of chord sequences and how the perception underlying a pitch class can affect the statistical learning of chord sequences. Neuromagnetic responses to two chord sequences of augmented triads that were presented every 0.5s were recorded from fourteen right-handed participants. One sequence was a ser...

Even though chord roots constitute a fundamental concept in music theory, existing models do not explain and determine them to full satisfaction. We present a new method which takes sequential context into account to resolve ambiguities and detect nonharmonic tones. We extract features from chord pairs and use a decision tree to determine chord roots. This leads to a quantitative improvement in...

We developed a computational model for automatically voicing chords based on a Bayesian network. Automatic chord voicing is difficult because it is necessary to choose extended notes and inversions by taking into account musical simultaneity and sequentiality. We overcome this difficulty by inferring the most likely chord voicing using a Bayesian network model where musical simultaneity and seq...