Clarinet: A Music Retrieval System
A MIDI based approach for music recognition is proposed and implemented in this paper. Our Clarinet music retrieval system is designed to search piano MIDI files with high recall and speed. We design a novel melody extraction algorithm that improves recall results by more than 10%. We also implement 3 algorithms for retrieval-two self designed (RSA Note and RSA Time), and a modified version of the Mongeau Sankoff Algorithm. Algorithms to achieve tempo and scale invariance are also discussed in this paper. The paper also contains detailed experimentation and benchmarks with four different metrics. Clarinet achieves recall scores of more than 94%.
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MAESTRO
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Methods
Bridge-net •
ClariNet •
Convolution •
Dense Connections •
Dilated Causal Convolution •
Dropout •
DV3 Attention Block •
DV3 Convolution Block •
GLU •
L1 Regularization •
Leaky ReLU •
Mixture of Logistic Distributions •
Normalizing Flows •
ReLU •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Softsign Activation •
WaveNet •
Weight Normalization
