What are the most effective visualization techniques for audio mining?

1 Waveforms and spectrograms

The most basic visualization techniques for audio mining are waveforms and spectrograms. Waveforms show the amplitude of the sound signal over time, while spectrograms show the frequency spectrum of the sound signal over time. Waveforms and spectrograms can help you identify the duration, loudness, pitch, and timbre of the audio, as well as the presence of silence, noise, or events. However, waveforms and spectrograms are not very informative for complex audio data, as they can be hard to interpret and compare.

2 Feature extraction and dimensionality reduction

To overcome the limitations of waveforms and spectrograms, you can use feature extraction and dimensionality reduction techniques to transform your audio data into more meaningful and manageable representations. Feature extraction involves extracting relevant characteristics from the audio signal, such as energy, entropy, zero-crossing rate, mel-frequency cepstral coefficients, or chroma features. Dimensionality reduction involves reducing the number of features or dimensions, while preserving the most important information, using techniques such as principal component analysis, t-distributed stochastic neighbor embedding, or autoencoders. Feature extraction and dimensionality reduction can help you visualize the similarity, diversity, and clustering of your audio data.

3 Scatter plots and heat maps

One of the most common ways to visualize the results of feature extraction and dimensionality reduction is to use scatter plots and heat maps. Scatter plots show the distribution of your audio data in a two-dimensional or three-dimensional space, where each point represents an audio sample or segment. Heat maps show the correlation or distance between your audio data in a matrix, where each cell represents the relationship between two audio samples or segments. Scatter plots and heat maps can help you identify outliers, clusters, and patterns in your audio data.

4 Parallel coordinates and radar charts

Another way to visualize the results of feature extraction and dimensionality reduction is to use parallel coordinates and radar charts. Parallel coordinates show the values of multiple features or dimensions for each audio sample or segment in a parallel axis plot, where each line represents an audio sample or segment. Radar charts show the values of multiple features or dimensions for each audio sample or segment in a circular plot, where each polygon represents an audio sample or segment. Parallel coordinates and radar charts can help you compare and contrast the attributes and differences of your audio data.

5 Sonification and audification

Finally, you can also use sonification and audification techniques to visualize your audio data by converting it into sound. Sonification involves mapping your audio data to sound parameters, such as pitch, volume, timbre, or rhythm, using a synthesis method, such as additive, subtractive, or granular synthesis. Audification involves playing back your audio data as sound, without any transformation or mapping, using a playback method, such as resampling, stretching, or filtering. Sonification and audification can help you perceive and explore the features and structures of your audio data using your auditory sense.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?