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Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species.

The goal of music highlight extraction, or thumbnailing, is to extract a short consecutive segment of a piece of music that is somehow representative of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, assuming that the most emotional part of a song usually corresponds to the highlight. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods to a heuristic energy-based method, a structural repetition-based method, and three other simple feature-based methods, respectively. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC-Pop 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate superior effectiveness of our methods over the competing methods. For reproducibility, we share the code and the pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.

This paper describes a method for obtaining a list of repeated chorus ("hook") sections in compact-disc recordings of popular music. The detection of chorus sections is essential for the computational modeling of music understanding and is useful in various applications, such as automatic chorus-preview/search functions in music listening stations, music browsers, or music retrieval systems. Most previous methods detected as a chorus a repeated section of a given length and had difficulty identifying both ends of a chorus section and dealing with modulations (key changes). By analyzing relationships between various repeated sections, our method, called RefraiD, can detect all the chorus sections in a song and estimate both ends of each section. It can also detect modulated chorus sections by introducing a perceptually motivated acoustic feature and a similarity that enable detection of a repeated chorus section even after modulation. Experimental results with a popular music database showed that this method correctly detected the chorus sections in 80 of 100 songs. This paper also describes an application of our method, a new music-playback interface for trial listening called SmartMusicKlOSK, which enables a listener to directly jump to and listen to the chorus section while viewing a graphical overview of the entire song structure. The results of implementing this application have demonstrated its usefulness. [ABSTRACT FROM AUTHOR]