Essays about: "Music classification"
Showing result 6 - 10 of 42 essays containing the words Music classification.
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6. Noise Robustness of CNN and SNN for EEG Motor imagery classification
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : As an able-bodied human, understanding what someone says during a phone call with a lot of background noise is usually a task that is quite easy for us as we are aware of what the information is we want to hear, e.g. the voice of the person we are talking to, and the information that is noise, e.g. READ MORE
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7. Comparison of CNN and LSTM for classifying short musical samples
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Applying machine learning to music and audio data is becoming increasingly common. One such area of research is instrument classification, which is the task of identifying the instrument played in a given audio file. In this study, we compared two machine learning model types, LSTM and CNN, on the task of classifying ten different instruments. READ MORE
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8. Efficient Music Thumbnailing for Genre Classification
University essay from KTH/Matematisk statistikAbstract : For music genre classification purposes, the importance of an intelligent and content-based selection of audio samples has been mostly overlooked. One common approach toward representative results is to select samples at predetermined locations. This is done to avoid analysis of the full audio during classification. READ MORE
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9. Comparison of Machine learningalgorithms on Predicting Churn withinMusic streaming service
University essay from Blekinge Tekniska HögskolaAbstract : Background: Customer churn prediction is one of the most popular part of bigbusinesses and often help the companies in customer retention and revenue generation.Customer churn may lead to huge loss of revenue and is important to analyzeand determine the cause for churn. READ MORE
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10. Organization of Electronic Dance Music by Dimensionality Reduction
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : This thesis aims to produce a similarity metric for tracks of the genre: Electronic Dance Music, by taking a high-dimensional data representation of each track and then project it to a low-dimensional embedded space (2D and 3D) by applying two Dimensionality Reduction (DR) techniques called t-distributed stochastic neighbor embedding (t-SNE) and Pairwise Controlled Manifold Approximation (PaCMAP). A content-based approach is taken to identify similarity, which is defined as the distances between points in the embedded space. READ MORE
