Structural Analysis

compute_sm_dot(X, Y)	Computes similarty matrix from feature sequences using dot (inner) product
plot_feature_ssm(X, Fs_X, S, Fs_S, ann, duration, color_ann=None, title='', label='Time (seconds)', time=True, figsize=(5, 6), fontsize=10, clim_X=None, clim=None)	Plot SSM along with feature representation and annotations (standard setting is time in seconds)
SSM_chroma(wav_filename:str, anno_csv: str, hop_size: int = 4096, Nfft: int = 1024)	To show the self-similarity matrix calculated by chroma
plot_self_similarity(y: npt.ArrayLike, sr: int, affinity: bool = False, hop_length: int = 1024)	To visualize the similarity matrix of the signal
compute_kernel_checkerboard_gaussian (L: int =10 , var: float = 0.5, normalize=True)	Compute Guassian-like checkerboard kernel
compute_novelty_ssm(S, kernel: npt.ArrayLike = None, L: int = 10, var: float = 0.5, exclude: bool =False)	Compute novelty function from SSM
SSM_Novelty(wav_filename:str, anno_csv: str)	To show the preview of self-similarity matrix calculated by Novelty function
SSM_Novelty_user_selection(wav_filename:str, anno_csv: str, save_to_csv: bool = True, L_filter: int = 11, hopsize: int = 5, L_kernel: int = 10)	To show the self-similarity matrix calculated by Novelty function