def visualizeFeaturesFolder(folder, dimReductionMethod, priorKnowledge = "none"):
    '''
    This function generates a chordial visualization for the recordings of the provided path.
    ARGUMENTS:
        - folder:        path of the folder that contains the WAV files to be processed
        - dimReductionMethod:    method used to reduce the dimension of the initial feature space before computing the similarity.
        - priorKnowledge:    if this is set equal to "artist"
    '''
    if dimReductionMethod=="pca":
        allMtFeatures, wavFilesList, _ = aF.dirWavFeatureExtraction(folder, 30.0, 30.0, 0.050, 0.050, compute_beat = True)
        if allMtFeatures.shape[0]==0:
            print("Error: No data found! Check input folder")
            return
        
        namesCategoryToVisualize = [ntpath.basename(w).replace('.wav','').split(" --- ")[0] for w in wavFilesList]; 
        namesToVisualize       = [ntpath.basename(w).replace('.wav','') for w in wavFilesList]; 

        (F, MEAN, STD) = aT.normalizeFeatures([allMtFeatures])
        F = np.concatenate(F)
        
        # check that the new PCA dimension is at most equal to the number of samples
        K1 = 2
        K2 = 10
        if K1 > F.shape[0]:
            K1 = F.shape[0]
        if K2 > F.shape[0]:
            K2 = F.shape[0]
        pca1 = sklearn.decomposition.PCA(n_components = K1)
        pca1.fit(F)        
        pca2 = sklearn.decomposition.PCA(n_components = K2)
        pca2.fit(F)        

        finalDims = pca1.transform(F)
        finalDims2 = pca2.transform(F)
    else:    
        allMtFeatures, Ys, wavFilesList = aF.dirWavFeatureExtractionNoAveraging(folder, 20.0, 5.0, 0.040, 0.040) # long-term statistics cannot be applied in this context (LDA needs mid-term features)
        if allMtFeatures.shape[0]==0:
            print("Error: No data found! Check input folder")
            return
        
        namesCategoryToVisualize = [ntpath.basename(w).replace('.wav','').split(" --- ")[0] for w in wavFilesList]; 
        namesToVisualize       = [ntpath.basename(w).replace('.wav','') for w in wavFilesList]; 

        ldaLabels = Ys
        if priorKnowledge=="artist":
            uNamesCategoryToVisualize = list(set(namesCategoryToVisualize))
            YsNew = np.zeros( Ys.shape )
            for i, uname in enumerate(uNamesCategoryToVisualize):        # for each unique artist name:
                indicesUCategories = [j for j, x in enumerate(namesCategoryToVisualize) if x == uname]
                for j in indicesUCategories:
                    indices = np.nonzero(Ys==j)
                    YsNew[indices] = i
            ldaLabels = YsNew

        (F, MEAN, STD) = aT.normalizeFeatures([allMtFeatures])
        F = np.array(F[0])

        clf = sklearn.discriminant_analysis.LinearDiscriminantAnalysis(n_components=10)
        clf.fit(F, ldaLabels)    
        reducedDims =  clf.transform(F)

        pca = sklearn.decomposition.PCA(n_components = 2)
        pca.fit(reducedDims)
        reducedDims = pca.transform(reducedDims)

        # TODO: CHECK THIS ... SHOULD LDA USED IN SEMI-SUPERVISED ONLY????

        uLabels = np.sort(np.unique((Ys)))        # uLabels must have as many labels as the number of wavFilesList elements
        reducedDimsAvg = np.zeros( (uLabels.shape[0], reducedDims.shape[1] ) )
        finalDims = np.zeros( (uLabels.shape[0], 2) ) 
        for i, u in enumerate(uLabels):
            indices = [j for j, x in enumerate(Ys) if x == u]
            f = reducedDims[indices, :]
            finalDims[i, :] = f.mean(axis=0)
        finalDims2 = reducedDims

    for i in range(finalDims.shape[0]):            
        plt.text(finalDims[i,0], finalDims[i,1], ntpath.basename(wavFilesList[i].replace('.wav','')), horizontalalignment='center', verticalalignment='center', fontsize=10)
        plt.plot(finalDims[i,0], finalDims[i,1], '*r')
    plt.xlim([1.2*finalDims[:,0].min(), 1.2*finalDims[:,0].max()])
    plt.ylim([1.2*finalDims[:,1].min(), 1.2*finalDims[:,1].max()])            
    plt.show()

    SM = 1.0 - distance.squareform(distance.pdist(finalDims2, 'cosine'))
    for i in range(SM.shape[0]):
        SM[i,i] = 0.0;


    chordialDiagram("visualization", SM, 0.50, namesToVisualize, namesCategoryToVisualize)

    SM = 1.0 - distance.squareform(distance.pdist(F, 'cosine'))
    for i in range(SM.shape[0]):
        SM[i,i] = 0.0;
    chordialDiagram("visualizationInitial", SM, 0.50, namesToVisualize, namesCategoryToVisualize)

    # plot super-categories (i.e. artistname
    uNamesCategoryToVisualize = sort(list(set(namesCategoryToVisualize)))
    finalDimsGroup = np.zeros( (len(uNamesCategoryToVisualize), finalDims2.shape[1] ) )
    for i, uname in enumerate(uNamesCategoryToVisualize):
        indices = [j for j, x in enumerate(namesCategoryToVisualize) if x == uname]
        f = finalDims2[indices, :]
        finalDimsGroup[i, :] = f.mean(axis=0)

    SMgroup = 1.0 - distance.squareform(distance.pdist(finalDimsGroup, 'cosine'))
    for i in range(SMgroup.shape[0]):
        SMgroup[i,i] = 0.0;
    chordialDiagram("visualizationGroup", SMgroup, 0.50, uNamesCategoryToVisualize, uNamesCategoryToVisualize)
This function generates a chordial visualization for the recordings of the provided path.
    ARGUMENTS:
        - folder:        path of the folder that contains the WAV files to be processed
        - dimReductionMethod:    method used to reduce the dimension of the initial feature space before computing the similarity.
        - priorKnowledge:    if this is set equal to "artist"