# Automatic Segmentation of Ethnomusicological Field Recordings
## 3 Results
### Labelling  
While there is a large number of published works dedicated to speech/music classification, they mostly deal with broadcast recordings, where their per-frame classification accuracies reach over 90% for classification into speech or music. Results may depend highly on the dataset, as recent Mirex music/speech detection results also show [7; 10]. We are not aware of other works dealing specifically with field recordings, so to put our work into perspective, we compare it to three other approaches on the same dataset:* A similar residual network architecture, but without music-specific filters in the input layer (12 3 \(\times\) 3 filters were used), without zero-mean convolutions and data augmentation during training [16]. The network was trained on 64 channel mel spectrograms;
* a multilayer perceptron with one hidden layer of 16 neurons trained on the VGGish [11] features extracted from the data. VGGish are audio classification features extracted from a VGG-like deep model trained on a large YouTube dataset and made available by Google. Input to the MLP consisted of two consecutive 128-dimensional VGGish vectors, each summarizing 1 s of audio; and
* a simple logistic regression model trained on hand-crafted features, as described in [1]. Three-fold cross validation was used to assess the performance of each approach, where two thirds of the dataset was used for training, the remaining third for testing, and the procedure repeated three times. The train/test split was done on audio files, so the audio from the test set never appeared in the training data. Average classification accuracies and the number of parameters of each approach are listed in Table 1. The proposed model outperforms the others. It has approximately the same number of parameters as our previous approach; however, the music specific filters with zero-mean convolutions, combined with data augmentation, increased its accuracy. Both approaches use batch normalization and l2 regularization during training to avoid overfitting. In addition, a third of the dataset is used for testing at each run, so it is safe to assume that the performance is realistic for a wide variety of materials. The small neural network based on the VGGish features also performed well, and analysis showed that this approach makes a higher number of errors when discerning solo singing from speech, as well as choir singing from solo. These classes are likely underrepresented in the AudioSet dataset, so the features do not capture the distinctions well enough. Our previous approach based on a small set of carefully chosen features and a simple logistic regression classifier still achieves an almost 80% accuracy. As it is fast to calculate, it represents a viable option for classification in scenarios where speed is important and GPU not available (it is approximately seven times faster than the deep learning approach on a consumer grade GPU). An analysis of errors showed that many mistakes are logical and can be attributed to several factors. First, some of the recordings are very noisy and even a human listener can have some difficulty discerning the contents. Such recordings are often mistakenly classified as instrumentals, as the noise is considered part of the performance. The confusion matrix in Table 2 shows that many mistakes are made between neighboring classes: Solo singing is misclassified as choir singing or speech, choir mostly as solo, instrumentals as choir, or speech as solo. Some confusions may be due to the particularity of the contents, e.g., some short excerpts of dialectal speech may sound very much like singing, when old people sing, they may sound similar to speech. Chanting is also problematic, as it borders on speech and singing, while choir parts sung in unison, labelled as choir singing in our dataset, may sound very similar to solo singing and are often confused as such. Some mistakes are not really mistakes--an excerpt may be correctly classified, and wrongly labelled.