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Searching sounds by text labels is often difficult, as text labels cannot always provide sufficient information for the sound content. Previously we proposed an unsupervised system called IMISOUND for sound search by vocal imitation. In this paper, we further propose a Convolutional Semi-Siamese Network (CSN) called IMINET. IMINET uses two towers of Convolutional Neural Networks (CNN) to extract features...
Multi-pitch analysis of polyphonic music requires estimating concurrent pitches (estimation) and organizing them into temporal streams according to their sound sources (streaming). This is challenging for approaches based on audio alone due to the polyphonic nature of the audio signals. Video of the performance, when available, can be useful to alleviate some of the difficulties. In this paper, we...
Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper,...
Vocal imitation is widely used in human communication. In this paper, we propose an approach to automatically recognize the concept of a vocal imitation, and then retrieve sounds of this concept. Because different acoustic aspects (e.g., pitch, loudness, timbre) are emphasized in imitating different sounds, a key challenge in vocal imitation recognition is to extract appropriate features. Hand-crafted...
Traditional tonality mode (major or minor) classification or audio key finding algorithms often rely on tonic annotations (key names) of the training songs. However, unlike classical music whose keys are usually explicitly labeled in their titles, the keys of numerous popular music are hard to obtain. In contrast, it is much easier to only label the mode for each song. With only modes labeled, traditional...
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