# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 4 Discussion  
The obtained results confirm that the proposed method is capable of predicting the three spatial audio scenes in the binaural recordings of music with a relatively good accuracy, ranging from 74% to 98% (depending on the BRIR set used to synthesize the recordings). The results could be regarded as satisfactory in view of the fact that the method was tested in the reverberant conditions, with an unknown number of foreground objects, with unknown signal characteristics of audio objects, and in a stationary head scenario. The obtained classification accuracy is comparable to the one reached in the pilot test (84%) undertaken by the present authors [20]. While the intra-BRIR generalizability of the method can be regarded as satisfactory, the inter-BRIR generalizability of the method still needs to be improved. The likely reason for the encountered difficulty in the developing of a generalizable model was due to substantial diversity between the recordings synthesized using various BRIR sets. It is expected that the inter-BRIR generalizability could be improved by incorporating a larger number of BRIR sets. Another way of improving the inter-BRIR generalizability might involve a multiconditional training (e.g., under noisy conditions [6]). It is also hypothesized by the authors that incorporating additional recordings in the audio corpus, synthesized using a broad range of head-related transfer functions, could also improve the performance and generalizability of the proposed method. According to expectations, the binaural cues proved to be the most prominent features in terms of the classification of the spatial audio scenes in binaural recordings of music. However, the MFCCs emerged to be almost as influential as the binaural cues. This outcome could be regarded as surprising since the MFCCs are commonly assumed to be the spectral descriptors [51], not the spatial ones. This study shows that they can also be used as a carrier of spatial information. A possible explanation of this observation could be linked to the mechanism of human hearing responsible for the front-back discrimination, which is predominantly based on the spectral weighting [16]. Since the MFCCs are particularly effective in describing a spectral envelope, it is hypothesized by the present authors that they capture the pinna-related filter characteristics (peaks and notches in the frequency response) responsible for the front-back discrimination. Most of the BRIR sets used in this study could be characterized as sparse with regards to their spatial resolution. The \"missing\" impulse responses were interpolated using a vector-base amplitude-panning algorithm [43]. While this method became a de facto standard in the area of spatial audio research, according to its author the method was valid under the assumption that the room was not very reverberant [43]. This assumption was violated for some of the BRIR sets used in this study, which could have an adverse effect on the developed method, for example, by degrading the generalizability of the proposed method for the BRIR sets exhibiting long reverberation times. Another limitation of the study, potentially degrading the performance of the proposed method, was related to the acoustic mirroring technique used to generate the missing BRIRs, as explained in detail in Section 2. While such an approach was explicitly recommended by the developers of some of the BRIR repositories [37], it was valid under the assumption that the room was symmetric with respect to the position of the dummy head microphone during the measurements of the BRIRs. Considering the architectural drawings accompanying some of the BRIR sets [37], the above assumption was violated. According to the informal listening tests undertaken by the authors, the departure from the above assumption did not affect the perception of the spatial scenes exhibited by the synthesized binaural audio excerpts.