# Automatic Segmentation of Ethnomusicological Field Recordings  
[PERSON]  
1 Faculty of Computer and Information Science, University of Ljubljana, Vecna pot 113, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (C.B.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (M.  
discriminate. Many approaches use standard timbral features, such as Mel Frequency Cepstral Coefficients [1] or chroma features [2], or develop new features, such as noise robust discriminators [3] or musically-informed Continuous Frequency Activation [4]. Based on the features, authors use different approaches to segment recordings, such as joint classification and segmentation by using a combination of standard hidden Markov models and multilayer perceptrons [5] or using a region growing algorithm to identify regions likely to contain speech or music followed by a maximum likelihood model that maximizes the probability of class labels given frame-level features and segment length limits [6].  
Recently, as in other domains, deep neural networks are increasingly used for audio segmentation. In the Mirex 2015 music/speech classification and detection task [7], 11 authors submitted their classification algorithms, where three were based on deep neural networks, including the best system [8] (99.7% accuracy) that used one convolutional and one fully connected layer to classify log-Mel spectrograms as speech or music. [PERSON] et al. [9] also explored deep neural networks for segmentation of broadcast signals, where they applied several fully connected layers over MFCC and chroma (CENS) features to discriminate between music/non-music, speech/non-speech, and noise/signal. Reported accuracies ranged from over 99% for silence/signal detection and 98% for speech/non-speech discrimination, down to 93% for music/non-music discrimination. In the Mirex 2018 music and speech detection task [10], all five authors used variants of different deep architectures for the segmenting TV broadcasts, where accuracies reached around 90% for speech and music detection. A more general audio classification approach was presented recently by authors from Google [11], who compared convolutional architectures for labelling audio from 70 M videos, where the best performing inception and residual networks achieved an AUC score of over 0.91 for classification of audio into 3000 classes. They also showed that features from the trained networks can be used to boost performance for other audio labelling tasks, such as predicting labels on the AudioSet dataset [12].  
In this paper, we explore deep neural networks for labelling and segmenting ethnomusicological field recordings. Unlike broadcast recordings, field recordings are more challenging to label and segment due to their noisy nature. This was well demonstrated in the Mirex'15 music/speech segmentation task, which used a dataset that included several field recordings. The best detection system was based on hand-crafted features and a simple logistic regression classifier trained on field recordings [1]. It outperformed deep neural networks and achieved an average frame-based accuracy of 89%, 10% less than on the simpler clean speech/music discrimination task. We describe the dataset gathered for our experiments, which we make available to the community, the methods used for labelling and segmentation, and the results achieved, as well as the tools developed in the process.