Dtw for dynamic time warping distance

Here is an example of how you could implement time series clustering of audio files using the Speech Accent Archive dataset in R:

# Load required libraries

data <- read.csv("speech-accent-archive/speech_data.csv")

# Extract relevant features from the audio data

dist_dtw <- dist(features, method="dtw")

# Select a clustering algorithm: k-means

sil_dtw <- silhouette(km_dtw$cluster, dist_dtw)

# Visualize the clusters using a scatter plot

cv_dtw <- cv.kmeans(features, km_dtw$centers, nstart=20)

This code first loads the required libraries, including tuneR for audio data processing, dtw for dynamic time warping distance, and cluster for clustering algorithms. It then loads the audio data from the speech_data.csv file and extracts relevant features using the extractFeatures function from the tuneR library. Next, it calculates the distance between the audio files using both Euclidean distance and dynamic time warping, and uses these distances to perform k-means clustering. The number of clusters is determined using the silhouette score as a Cluster Validity Index. Finally, the clusters are visualized using a scatter plot and the quality of the clustering results is evaluated using cross-validation.