Latent Birds:A Bird's-Eye View Exploration of the Latent Space

The use of a generative approach for sound synthesis breaks through the limitations of traditional approaches, proposing novel ways to explore creative ideas. This paper demonstrates a method to generate original bird vocalizations using a Variational Convolutional Autoencoder trained on mel-spectrograms of bird song and call recordings. The vocalizations are reconstructed by sampling the latent space and decompressing the resulting mel-spectrogram. The results are quite promising, in that our system is able to generate a variety of bird vocalizations depicting plausible songs and calls, by interpolating between existing vocalizations or sampling the latent space. A Twitter bot that publishes a unique daily bird vocalization is also implemented

Klasifikasi Suara Untuk Memonitori Hutan Berbasis Convolutional Neural Network

Forest has an important role on earth. The need to monitor forest from illegal activities and the types of animals in there is needed to keep the forest in good condition. However, the condition of the vast forest and limited resource make direct forest monitoring by officer (human) is limited. In this case, sound with digital signal processing can be used as a tool for forest monitoring. In this study, a system was implemented to classify sound on the Raspberry Pi 3B+ using mel-spectrogram. Sounds that classified are the sound of chainsaw, gunshot, and the sound of 8 species of bird. This study also compared pretrained VGG-16 and MobileNetV3 as feature extractor, and several classification methods, namely Random Forest, SVM, KNN, and MLP. To vary and increase the number of training data, we used several types of data augmentation, namely add noise, time stretch, time shift, and pitch shift. Based on the result of this study, it was found that the MobileNetV3-Small + MLP model with combined training data from time stretch and time shift augmentation provide the best performance to be implemented in this system, with an inference duration of 0.8 seconds; 93.96% accuracy; and 94.1% precision

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Anàlisi del paisatge sonor i detecció automàtica d'esdeveniments acústics a l'aeroport Josep Tarradellas Barcelona - El Prat

Avui en dia, l'automatització de processos amb grans volums de dades està arribant a tots els sectors. Degut a l'impacte que crea l'aeroport de Josep Tarradellas Barcelona - El Prat, en l'espai natural del Delta del Llobregat que se situa al costat, s'ha volgut fer un anàlisi del paisatge sonor, a més a més, d'automatitzar el procés de la detecció d'esdeveniments acústics d'aquest. Per dur-ho a terme, s'ha fet un estudi de camp de treball del Delta del Llobregat on posteriorment es duen a terme 3 gravacions de dues hores cadascuna per a fer possible la creació d'un dataset, on després s'apliquen algoritmes d'aprenentatge automàtic o més coneguts com algoritmes de Machine Learning que ens ajuden a automatitzar el procés de detecció