Today we continue our PyDataSci series joined by Brian McFee, assistant professor of music technology and data science at NYU, and creator of Librosa, a python package for music and audio analysis.
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In our conversation, Brian walks us through his experience building Librosa, detailing the core functions provided in the library, and his experience working in Jupyter Notebook. We explore typical Librosa workflow, looking at which, if any, of the internal components directly performed learning tasks, and discuss trying to stay ahead of the curve by being an early adopter of various frameworks and applications.
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Thanks to our Sponsor!
I’d like to send a huge thanks to our sponsor for this series, IBM. IBM has a long history of engaging in and supporting open source projects that are important to enterprise data science — projects like Hadoop, Spark, Jupyter, Kubernetes, and Kubeflow.
IBM also hosts the IBM Data Science Community, which is a place for enterprise data scientists looking to learn, share, and engage with their peers and industry renowned practitioners. There you’ll find informative tutorials and case studies, Q&As with leaders in the field, and a lively forum covering a variety of topics of interest to beginning and experienced data scientists.
Visit the IBM Data Science Community at ibm.com/community/datascience.
From the Interview
- Paper: M. Müller Signal Processing for Music Analysis
- Python Feather
- Python Arrow
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“More On That Later” by Lee Rosevere licensed under CC By 4.0