# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology
### Audio Features  
As known, audio features play a vital role in the process of identifying change points. In order to analyze the different features, we choose different categories of feature based on their relevance to different aspects of the audio signal, can be summarized as follows:  
* Mel-frequency cepstral coefficients (MFCC)--captures the timbral and phonetic content, which is useful for distinguishing different speakers. * Mel-spectrogram--similar to the spectrogram but focused on the mel scale, which is more aligned with human hearing. * Chroma--captures the harmonic content and pitch class, which can indicate tonal changes that may differentiate speakers. * Spectral centroid--represents the \"center of mass\" of the spectrum, which often relates to the brightness of a sound. * Spectral bandwidth--measures the spread of the spectrum, providing information about timbre and energy distribution. * Spectral contrast--captures the difference between peaks and valleys in a spectrum, highlighting spectral variation which may signal speaker changes. * Spectral rolloff--indicates the frequency below which a certain percentage of the total spectral energy is contained, useful for tracking energy shifts. * Root mean square (RMS) energy--represents the power of the audio signal, which can help distinguish speakers based on loudness and energy pattern. * Pitch--provides fundamental frequency information that can help differentiate speakers based on vocal pitch. * Zero crossing rate--indicates the rate of signal sign changes, which can be useful for detecting changes in speech quality and distinguishing between different speakers. Feature Changes vs. Speaker Changes  
In the above we briefly describe each feature, in order to analyze those features how they useful to speaker change detection, the meetings audio from AMI corpus [31] is used in the following statistical counting. This section outlines the process of detecting changes in audio features and analyzing their co-occurrence with speaker changes. By identifying points where specific features change (FC), we investigate how these changes align with speaker changes (SC) to inform feature selection for speaker change detection. For each audio feature, values are extracted over time, and points of significant change are identified by analyzing the differences between consecutive frames. This method accommodates both one-dimensional and two-dimensional feature data: (i) one-dimensional features (such as pitch) are straightforward; absolute differences between consecutive values are calculated to capture changes over time. (ii) two-dimensional features (such as MFCCs) involve multiple components per time frame. In this case, differences across dimensions are aggregated using the mean, which reflects overall changes in the feature values. Using the mean helps capture general trends across all components of a feature, making it suitable for analyzing broad patterns in audio data. Thresholds are applied to these feature differences to mark significant change points. By adjusting the threshold, it is possible to control the sensitivity of change detection for each feature. After determining the change times for each feature, co-occurrence analysis is conducted to assess how often these feature changes align with speaker changes. A defined time window, set by the tolerance parameter, is used to determine if two events are co-occurred. A tolerance was set to 1 s, any feature change within this window of a speaker change is marked as a co-occurrence. In the Figure 2, we plot thresholds against the probabilities \(P(FC|SC)\) and \(P(SC|FC)\), which represent the likelihood of feature changes given speaker changes, and vice versa, across various audio features. Figure 2: Probability distributions of speaker change (SC) and feature change (FC) across corresponding threshold values for various audio features. Across all graphs, \(P(FC|SC)\) generally starts at higher values at lower thresholds, indicating greater sensitivity to minor feature changes when a speaker change occurs.