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Authors
Advisor(s)
Abstract(s)
Every day, thousands of new songs are created and distributed over the internet.
These ever-increasing databases introduced the need for automatic search and organization
methods, that allow users to better filter and browse such collections. However, fundamental
research in the MER field is very academic, with the typical work presenting results in the
form classification metrics – how good the approach worked in the tested datasets and
providing access to the data and methods.
In order to overcome this problem, we built and deployed a platform to orchestrate a
distributed, resilient, and scalable, music emotion recognition (MER) application using
Kubernetes that can be easily expanded in the future. The solution developed is based on a
proof of concept that explored the usage of containers and microservices in MER but had
some gaps. We reengineered and expanded it, proposing a properly orchestrated, containerbased solution, and adopting a DevOps development culture with continuous integration (CI)
and continuous delivery (CD) that in an automated way, makes it easy for the different teams
to focus on developing new blocks separately.
At the application level, instead of analyzing the audio signal recurring to only three
audio features, the system now combines a large number of audio and lyric (text) features,
explores different parts of audio (vocals, accompaniment) in segments (e.g., 30-second
segments instead of the full song) and uses properly trained machine learning (ML)
classifiers, a contribution by Tiago António. At the orchestration level, it uses Kubernetes
with Calico as the networking plugin, providing networking for the containers and pods and
Rook with Ceph for the persistent block and file storage. To allow external traffic into the
cluster, will use HAproxy as an external ingress controller on an external node, with BIRD
providing BGP peering with Calico, allowing the communication between the pods and the
external node. ArgoCD was selected as the continuous delivery tool, constantly syncing with
a git repository, and thus maintaining the state of the cluster manifests up to date, which
allows totally abstracting developers from the infrastructure. A monitoring stack combining
Prometheus, Alertmanager and Grafana allows the constant monitoring of running
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applications and cluster status, collecting metrics that can help to understand the state of
operations. The administration of the cluster can be carried out in a simplified way using
Portainer. The continuous implementation pipelines run on GitHub Actions, integrating
software and security tests and automatically build new versions of the containers based on
tag releases and publish them on DockerHub. This implementation is fully cloud native and
backed only by open source software.
Description
Keywords
Music emotion recognition Containers Orchestration Kubernetes DevOps