## 4 Evaluation
### Comparison of Various Features  
Table 1 compares the proposed and conventional features that are commonly used in ASC and SED applications. The abbreviations MelSpec, Log-Mel, GAM, and CQT denote the Mel-spectrogram, log-Mel spectrum, gammatonegram, and constant-Q transform (CQT), respectively. The frequency basis matrix used to extract the NMF features in Table 1 was trained using strongly-labeled data only as it exhibited good overall performance. The effect of the data on the frequency basis training will be considered in the next subsection. The dimensions of the extracted features were all set to 128, similar to that in the Delphin-Poulet system [40], to compare the performances of the features without changing the structure of the classifier. The CQT was performed under 16 bins per octave and 32.7 Hz  
lower bound frequency. In total, 128 CQT bins (=8 octaves) corresponded to a frequency range of less than approximately 8 kHz. The proposed system was also compared with the Cornell system [46] including the log-Mel features with data augmentation, per-channel energy normalization (PCEN), mean-teacher CRNN, and post-processing with hidden Markov model (HMM), which was submitted to DCASE 2020 Task 4. The details of the subsystems can be found in [46]. The training parameters (e.g., the weight for the consistency costs, number of epochs, batch size, and the moving-average weighting factor of the mean-teacher model) were set to the same values of the proposed system. Because the Cornell system has its own post-processing consisting of HMM and random forest optimization, the proposed event-wise post-processing was not applied to the Cornell system. The performances of the NMF methods are comparable to those of the Mel-spectrogram and gammatonegram, the best among the conventional features, and are better than those of the log-Mel spectrum and CQT in all the performance measures. The comparison between the micro-averaged performance of the NMF and that of the Mel-spectrogram revealed that the NMF methods exhibited slightly better performances without the event-wise post-processing and slightly worse performances with the event-wise post-processing. The gammatonegram had the best performance in the macro-averaged F1-score among the features but demonstrated low performance in the micro-averaged F1-score. Moreover, the features extracted using the iterative and non-iterative NMF methods exhibited similar performances. Thus, the non-iterative NMF method can be considered as an alternative to the iterative NMF method. The micro-averaged and macro-averaged F1-scores of the proposed system are similar to and slightly less than that of the Cornell system, respectively. We think that the difference of the performance was caused by additional sub-systems in the Cornell system, such as PCEN, data augmentation, and random forest optimization of HMM post-processing. Table 2 presents the class-wise F1-score performances. The gray background denotes the class where the NMF method exhibits better performance than that in the Mel-spectrogram, and the bold-face number stands for the best performance in each class. A scan be seen in the table, the NMF methods are advantageous for classes with harmonic structures (speech, dog, cat, and alarm bell) and disadvantageous for noise-like classes (electric shaver, blender, running water, and vacuum cleaner). The class-wise performances of the iterative and non-iterative NMFs are different. The _iterative_ NMF has better performances for five classes (electric shaver, speech, frying, blender, and vacuum cleaner), similar performances for two classes (cat and dog), and worse performances for three classes (dishes, running water, and alarm bell). The last three classes (dishes, running