![]() By leveraging advanced AI algorithms, Musicfy enables users to transform their songs and covers with ease. Our basic LSTM model had only one LSTM layer so we could learn more about the effect of adding more layers.Musicfy is a cutting-edge AI-powered music tool that provides musicians with a variety of features to enhance their music production process. We know that for models such as OpenUnmix’s model library and Magenta’s music generation model, multiple LSTM layers are defined in the model. If we had time, we could have debugged this issue further by evaluating the weights coming in and out of each layer. We found that data normalization helped control exploding gradients. ![]() We adjusted this by decreasing our learning rate, but by doing so we could have sacrificed some of the training capabilities of our model. We likely would have seen better results if we trained for longer and had faster hardware.Īnother challenge of our model was handling exploding gradients. Since we are dealing with a lot of data, processing, and intense neural networks, we were only able to train our networks for a small number of epochs. This better performance can likely be attributed to LSTM’s temporal stability.Ī significant portion of our non-spectacular results can be attributed to the models not being trained to completion. However, qualitatively the result from the LSTM network does have less distortion and does seem to reduce percussion and bass significantly. This architecture is very similar to our Multilayer Perceptron model, but with the significant added complexity that the data being handled by the network now has the extra dimension of time.Īdmittedly, the audio that was passed through both of the networks came out very distorted. The LSTM network, therefore, should introduce fewer audio artifacts because unlike a Multilayer Perceptron, the LSTM network has temporal stability (each output should make sense relative to the previous).įor our LSTM neural network, we simply added a single unidirectional LSTM layer to three fully connected layers. In fact, music is much more about the change in frequencies over time than the specific frequencies themselves. This is because audio relies on events that happen over time. LSTM networks have now become the most common method for neural network audio processing. LSTM networks are especially useful for dealing with inputs thatĬhange over time because as their name suggests, they perform long-termĪnd short-term memory on a piece of data, picking up on both immediate and Source: Understanding LSTMs, Colah's BlogĪs pictured in the diagram, gates are simply the sigmoid expression multiplying a term because the sigmoid expression squashes values between one and zero, and multiplying one or zero by an expression allows data to pass or block it through the gate. The LSTM cells accomplish this behavior by having “gates”, which based on data and trained weights decide whether to keep previous memory (known as “cell state”), decide whether to store data into the cell state, and finally passes on the cell state to the next cell. If these inputs are pieces of data across time, then the LSTM will determine the output of the current state in part from the previous states. Each cell determines what memory to pass onto the next cell based on its piece of data, the previous piece of data, and the memory that was passed to it. LSTM works by connecting a series of different inputs to the network in a chain of LSTM cells which pass memory through the series. We will walk through how these networks work, and how they are applicable Long short-term memory (LSTM) is a type of recurrent neural network used to process data ![]() Once the algorithm successfully isolates these frequencies, they can be filtered out and separated accordingly. Many groups, such as SigSep and Google’s Magenta Research group, have successfully used LSTM neural networks to recognize patterns in vocals and instrumentation. ![]() This complex set of problems makes this a well suited situation for a neural network because a neural network will learn to identify and isolate the frequencies associated with vocalization.ĭesigning a machine learning model which separates these parts is difficult, but not impossible. In addition, the frequencies from each of these parts often overlap with each other, meaning separation is not as simple as recognizing that an instrument is playing and completely eliminating every frequency that the instrument plays. To isolate a part from the rest of the song, we must correctly identify the overtones and undertones associated with the part. For each instrument or voice, there are a set of undertones and overtones that give it its unique sound, otherwise known as timbre. One of the main challenges of stem splitting is isolating the correct frequencies. The Applications of Machine Learning in Stem Splitting ![]()
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