Define Function : Music Genre Classification

def check_genre(song, sr, model) :

    col_names = ['chroma_stft_mean', 'chroma_stft_var',	'rms_mean',	'rms_var',	
                 'spectral_centroid_mean', 'spectral_centroid_var',	'spectral_bandwidth_mean',
                 'spectral_bandwidth_var', 'rolloff_mean', 'rolloff_var', 'zero_crossing_rate_mean',
                 'zero_crossing_rate_var', 'harmony_mean', 'harmony_var', 'perceptr_mean', 'perceptr_var',
                 'tempo', 'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean',	'mfcc2_var', 'mfcc3_mean', 'mfcc3_var', 
                 'mfcc4_mean','mfcc4_var', 'mfcc5_mean',	'mfcc5_var', 'mfcc6_mean', 'mfcc6_var',	'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean',
                 'mfcc8_var', 'mfcc9_mean',	'mfcc9_var', 'mfcc10_mean',	'mfcc10_var', 'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean',
                 'mfcc12_var', 'mfcc13_mean', 'mfcc13_var',	'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean',	'mfcc15_var', 'mfcc16_mean',
                 'mfcc16_var', 'mfcc17_mean', 'mfcc17_var',	'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean',	'mfcc19_var', 'mfcc20_mean', 'mfcc20_var']

    data = pd.read_csv("features_3_sec.csv");

    X = data.drop("label", axis = 1)
    X_droped = X.drop(["filename", "length"], axis = 1, inplace = False)

    chroma_shift = librosa.feature.chroma_stft(song, n_fft=2048, hop_length=512) # 음악의 크로마 특징
    rmse = librosa.feature.rms(song, frame_length=512, hop_length=512)           # RMS값
    spectral_centroids = librosa.feature.spectral_centroid(song, sr=sr)          # 스펙트럼 무게 중심
    spec_bw = librosa.feature.spectral_bandwidth(song, sr=sr)                    # 스펙트럼 대역폭
    spectral_rolloff = librosa.feature.spectral_rolloff(song, sr=sr)[0]          # rolloff
    zcr = librosa.feature.zero_crossing_rate(song, hop_length=512)               # zero to crossing
    y_harm, y_perc = librosa.effects.hpss(song)                                  # 하모닉, 충격파
    tempo, _ = librosa.beat.beat_track(song, sr=sr)                              # 템포
    mfcc = librosa.feature.mfcc(song, sr=sr,n_mfcc=20)                           # mfcc 20까지 추출

    features_extracted = np.hstack([                                    
                                    np.mean(chroma_shift),
                                    np.var(chroma_shift),
                                    np.mean(rmse),
                                    np.var(rmse),
                                    np.mean(spectral_centroids),
                                    np.var(spectral_centroids),
                                    np.mean(spec_bw),
                                    np.var(spec_bw),
                                    np.mean(spectral_rolloff),
                                    np.var(spectral_rolloff),
                                    np.mean(zcr),
                                    np.var(zcr),
                                    np.mean(y_harm),
                                    np.var(y_harm),
                                    np.mean(y_perc),
                                    np.var(y_perc),
                                    tempo,
                                    np.mean(mfcc.T, axis=0),
                                    np.var(mfcc.T, axis=0)
                                                            ])

    features = features_extracted.reshape(1, 57)

    input_df = pd.DataFrame(features, columns = col_names)

    df_concated = pd.concat([X_droped, input_df], axis = 0)

    ss = StandardScaler()
    concat_scaled = ss.fit_transform(np.array(df_concated.iloc[:, :], dtype = float))

    concat_df = pd.DataFrame(concat_scaled, columns = col_names)
    
    input_df = concat_df.iloc[-1]
    input_df = pd.Series.to_frame(input_df)
    input_arr = input_df.to_numpy()
    input_arr = input_arr.reshape(1, 57)

    input_df = pd.DataFrame(input_arr, columns = col_names)

    prediction = model.predict(input_df)

    print("\n=====================")
    
    print("Predicted Label : {}".format(prediction))

    if prediction == 0:
        answer = "blues"
    elif prediction == 1:
        answer = "classical"
    elif prediction == 2:
        answer = "country"
    elif prediction == 3:
        answer = "disco"
    elif prediction == 4:
        answer = "hiphop"
    elif prediction == 5:
        answer = "jazz"
    elif prediction == 6:
        answer = "metal"
    elif prediction == 7:
        answer = "pop"
    elif prediction == 8:
        answer = "reggae"
    else:
        answer = "rock"

    print("This is {}!!".format(answer))
    print("=====================")

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