I need to extract sounds and their variances from music. Then I want to compare how many times they occur in other songs and the most used sounds in top music.
To extract sounds and their variances from music, you can use a music analysis library, such as Essentia or Librosa, to extract specific audio features such as pitch, timbre, and spectral content. Once you have extracted the audio features, you can then compare them across different songs to identify common sounds and variances.
Here are some general steps you can follow to extract sounds and variances from music and compare them across different songs:
Choose the audio features you want to extract: There are many audio features you can extract from music, depending on the specific sounds and variances you are interested in. Common audio features include pitch, timbre, and spectral content, but you can also extract features such as tempo, rhythm, and harmony.
Extract the audio features from your music dataset: Use a music analysis library, such as Essentia or Librosa, to extract the audio features from your music dataset. Make sure to store the extracted features in a structured format, such as a CSV or JSON file, to enable easy retrieval and analysis.
Compare the sounds and variances across different songs: Once you have extracted the audio features, you can compare them across different songs to identify common sounds and variances. This could involve using clustering algorithms, such as k-means or hierarchical clustering, to group similar sounds together, or using statistical analysis techniques, such as principal component analysis (PCA), to identify the most important audio features that distinguish different songs.
Identify the most used sounds in top music: You can use the same techniques described above to identify the most used sounds in top music. This could involve analyzing a large dataset of popular songs, such as the Billboard Hot 100 chart, to identify common sounds and variances that are present across many different songs.
Visualize and interpret the results: Once you have compared the sounds and variances across different songs and identified the most used sounds in top music, you can visualize and interpret the results. This could involve creating plots or heatmaps to visualize the distribution of different sounds and variances across different songs, or using machine learning algorithms to classify songs based on their audio features.
It's worth noting that extracting and comparing sounds and variances from music can be a challenging task that requires a deep understanding of digital signal processing and machine learning. Additionally, the accuracy of the results can be highly dependent on the quality of the audio input and the specific audio features you choose to extract. So, you may need to experiment with different techniques and approaches to find the best solution for your specific needs.