It is basically, interpolating in the representation space that is just a linear combination.Here’s the dirty truth about how to DJ. In the context of this work, generating a weighted average of two patterns would give the well-known crossfading (gradually lowering the volume of one track while increasing the volume of the other track). Interpolating in the latent space works better because of the non-linear mappings from the input to the latent space and from the latent space to the output. Is it always true? It turns out, it actually is and the answer lies in the theory of deep learning. But, one might ask what is the reasoning behind this statement. The idea behind the proposed method is that interpolation in latent space will provide far better results than interpolation in the pattern (feature) space. The proposed method is taking two music patterns (each one represented as 6圆4 array), it encodes them using an encoder from a learned VAE model, then it interpolates between the latent representations of the two patterns and then decodes both patterns to give smooth transition patterns as output. Top row: Electro-Funk mid two rows: IDM bottom two rows: Techno. Pixel intensities correspond to MIDI velocities. Instruments from the top are (1): bass drum, (2): snare drum, (3): closed hi-hat, (4): open hi-hat, (5): rimshot, (6): cowbell.
Ten sample drum patterns in the EDM dataset. Each pattern is given as a 6 x 64 array, since all the generated patterns have length of 64.įig. Each pattern is represented as a two-dimensional array whose y-axis represents the 6 drum instruments and the x-axis represents the time. The dataset in the end consisted of 1782 drum patterns. The authors created a dataset of drum patterns of three popular electronic music genres: Electro, Techno and Intelligent Dance Music (IDM) ending up with 1–1.5 hours of music for each of the three genres to be used for their method. The music data representation, the architecture, as well as the interpolation and the whole method are explained below. The method is based on deep learning, utilizing Variational Autoencoders (VAEs) and interpolation in the latent space. In fact, Tijn Borghuis et al., propose a generative method that generates drum patterns which can be used to seamlessly transition different-genre tracks in the electronic dance music domain.
Recent work in machine learning (or more specifically deep learning), has given some answers and has provided useful methods for solving the problem of smooth transitioning between tracks of a different genre.
As I mentioned before, this is necessary in order to create a playlist that will express a certain mood or emotion rather than just creating a list of same-genre songs carrying no energy altogether. Maybe one of the most difficult tasks that DJs face is smooth transitioning between songs from a different genre. Good DJs are able to provide a seamless and perceptually smooth transition between two tracks, thus making a mix of different tracks sound like a single flowing one. In this way, the DJ is making a trade-off between the smooth, clean transition between tracks and his/her performance in terms of creating a natural mix of tracks that expresses a specific style and brings its own energy. It is a common and good practice among DJs to create and divide playlists by mood (aggressive, soulful, melancholy) and energy (slow, medium, fast) rather than by music genre. How Artificial Intelligence Can Help DJs Deliver a Seamless Mix
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