![]() This results in a CQT spectrogram C ∈ R T × 74 with T denoting the number of time frames. Around this core MIDI pitch range, we add a lower and upper pitch margin of 5 semitones to allow for on-the-fly pitch shift data augmentation as will be explained in Section 3.2. ![]() This range consists of 64 pitches and was chosen in order to replicate the network architecture proposed in. A constant-Q transformation (CQT) is computed with a hopsize of 512 samples, 12 bins per semitone resolution, and a core MIDI pitch range of (E1 to F5). In addition to a pitch estimation improvement, the voicing estimation performance is clearly enhanced.Īudio signals are mixed to mono and downsampled to a sample rate of 22.05 kHz. The U-net based method outperforms previous knowledge-driven and data-driven bass transcription algorithms by around five percentage points in overall accuracy. Using a training set that covers various music genres and a validation set that includes jazz ensemble recordings, we obtain the best transcription performance for a downscaled version of the reference algorithm combined with skip connections that transfer intermediate activations between the encoder and decoder. In a parameter importance study, we study the influence of the skip connection strategy between the encoder and decoder layers, the data augmentation strategy, as well as of the overall model capacity on the system’s performance. ![]() We investigate pitch shifting and random equalization as data augmentation techniques. In this paper, we adapt a recently proposed U-net deep neural network architecture from melody to bass transcription.
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