Brian McFee's guest appearances

Brian McFee

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Data Journeys

The So Strangely Podcast

Drew Conway

Kirk Borne

Andrew Ng

Rob Walker

Chris White

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

Lotte Bransen

Christopher Berner

David Spiegelhalter

Kristen Grauman

OwlTail

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.Brian walks us through his experience building LibROSA, including:• Detailing the core functions provided in the library • His experience working in Jupyter Notebook• We explore a typical LibROSA workflow &amp; more!The complete show notes for this episode can be found at twimlai.com/talk/26

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. Brian walks us through his experience building LibROSA, including: • Detailing the core functions provided in the library  • His experience working in Jupyter Notebook • We explore a typical LibROSA workflow &amp; more! The complete show notes for this episode can be found at <a href="http://twimlai.com/talk/26" rel="nofollow" target="_blank">twimlai.com/talk/26</a>

Librosa: Audio and Music Processing in Python with Brian McFee - TWiML Talk #263

Dr.  Brian McFee develops machine learning tools to analyze multimedia data. This includes recommender systems, image and audio analysis, similarity learning, cross-modal feature integration, and automatic annotation. As of Fall, 2014, he is a data science fellow at the Center for Data Science at New York University. Previously, he was a postdoctoral research scholar in the Center for Jazz Studies and LabROSA at Columbia University.   My conversation with Brian today was focused on discussing his research in music informatics and its many facets and applications. He tells about some of the methods he used during his dissertation, and I ask him for insight on how to get a recommender system to recommend stuff that you actually like.   Here are some of the highlights of the show:    [3:17] What came first for Brian, the data science or the music? [5:19] Of all the things he could have chose to study, why did Brian choose music? [7:35] What is it like to be in a branch of data science that has become so closely tied with industry and well understood by the public? [9:37] How has Brian’s work expanded his own taste in music, and given him an appreciation of jazz? [12:00] Brian gives a brief history of the field of music informatics. [14:48] Where was the field when Brian wrote his dissertation, “More like this: Machine learning approaches to music similarity?“ [17:15] How have the characteristics and features for making predictions about music evolved since then? [21:06] Why does the concept of genre generally irritate Brian, and what is the “David Bowie problem?” [26:20] How do you address the problem of subjectivity in the field when conducting research? [31:21] Is there a dilemma in trying to take a subjective art like music and trying to quantify it as a science? [35:24] How can a recommender system actually accurately predict what kind of music a listener is looking for? [38:43] What can you do to train your Spotify recommendations? [42:33] How do you make the career decision whether to stay in academia vs. go into industry? [46:31] What kind of problems is Brian currently interested in solving? [49:00] What major life lessons can be taken away from work in machine learning? [50:00] Rapid fire questions.    Enjoy the show!   Find more at www.ajgoldstein.com/podcast/ep24

Dr. <a href="https://twitter.com/functiontelechy?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" rel="nofollow" target="_blank"> Brian McFee</a> develops machine learning tools to analyze multimedia data. This includes recommender systems, image and audio analysis, similarity learning, cross-modal feature integration, and automatic annotation. As of Fall, 2014, he is a data science fellow at the Center for Data Science at New York University. Previously, he was a postdoctoral research scholar in the Center for Jazz Studies and LabROSA at Columbia University. My conversation with Brian today was focused on discussing his research in music informatics and its many facets and applications. He tells about some of the methods he used during his dissertation, and I ask him for insight on how to get a recommender system to recommend stuff that you actually like. Here are some of the highlights of the show: <ul> <li>[3:17] What came first for Brian, the data science or the music?</li> <li>[5:19] Of all the things he could have chose to study, why did Brian choose music?</li> <li>[7:35] What is it like to be in a branch of data science that has become so closely tied with industry and well understood by the public?</li> <li>[9:37] How has Brian’s work expanded his own taste in music, and given him an appreciation of jazz?</li> <li>[12:00] Brian gives a brief history of the field of music informatics.</li> <li>[14:48] Where was the field when Brian wrote his dissertation, “<a href="http://bmcfee.github.io/papers/bmcfee_dissertation.pdf" rel="nofollow" target="_blank">More like this: Machine learning approaches to music similarity</a>?“</li> <li>[17:15] How have the characteristics and features for making predictions about music evolved since then?</li> <li>[21:06] Why does the concept of genre generally irritate Brian, and what is the “David Bowie problem?”</li> <li>[26:20] How do you address the problem of subjectivity in the field when conducting research?</li> <li>[31:21] Is there a dilemma in trying to take a subjective art like music and trying to quantify it as a science?</li> <li>[35:24] How can a recommender system actually accurately predict what kind of music a listener is looking for?</li> <li>[38:43] What can you do to train your Spotify recommendations?</li> <li>[42:33] How do you make the career decision whether to stay in academia vs. go into industry?</li> <li>[46:31] What kind of problems is Brian currently interested in solving?</li> <li>[49:00] What major life lessons can be taken away from work in machine learning?</li> <li>[50:00] Rapid fire questions.</li> </ul> Enjoy the show! Find more at <a href="http://www.ajgoldstein.com/podcast/ep24" rel="nofollow" target="_blank">www.ajgoldstein.com/podcast/ep24</a>

#24: Brian McFee: Music and Data Science

Andrew Demetriou

Music tech and data science professor Brian McFee recommends Vocals in Music Matter: The Relevance of Vocals in the Minds of Listeners by Andrew Demetriou, Andreas Jansson, Aparna Kumar, and Rachel M. Bittner, published in the 2018 ISMIR proceedings. Brian and Finn interview Andrew Demetriou about this research combining descriptions of music on Spotify and survey responses on what users pay attention to, like, and dislike in music generally and vocals specifically. Time Stamps  [0:00:00] Introduction with Brian [0:10:05] Interview: Introduction: Origins of paper and Survey 1 analysis [0:20:15] Interview: Results of survey 1 and ethical research practices at Spotify [0:27:03] Interview: Second Survey construction, analysis, and results [0:34:37] Interview: Problems of terminology and labeling [0:43:27] Interview: Overall results and absence of vocals terms in music descriptions [0:53:30] Interview: Implications for everyday music listening [0:58:40] Closing with Brian (12/10 for efficient summary)  Show notes  Recommended article: Demetriou, A., Jansson, A., Kumar, A., &amp; Bittner, R. M. Vocals in Music Matter: The Relevance of Vocals in the Minds of Listeners. Proceedings of ISMIR 2018 (pp. 514-520).  Slide deck from the corresponding ISMIR talk that caught Brian’s attention   Interviewee: Andrew Demetriou Co-host: Prof. Brian McFee  And here is the action shot of the research team card sorting participants’ text responses to Survey 1.Spotify researchers (left to right) Rachel Bittner, Andreas Jansson, Andrew Demetriou, and Aparna Kumar working through the text responses to Survey 1.Credits The So Strangely Podcast is produced by Finn Upham, 2018. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.

Music tech and data science professor Brian McFee recommends <a href="http://ismir2018.ircam.fr/doc/pdfs/98_Paper.pdf" rel="nofollow" target="_blank">Vocals in Music Matter: The Relevance of Vocals in the Minds of Listeners</a> by Andrew Demetriou, Andreas Jansson, Aparna Kumar, and Rachel M. Bittner, published in the 2018 ISMIR proceedings. Brian and Finn interview Andrew Demetriou about this research combining descriptions of music on Spotify and survey responses on what users pay attention to, like, and dislike in music generally and vocals specifically. <h4>Time Stamps</h4> <ul> <li>[0:00:00] Introduction with Brian</li> <li>[0:10:05] Interview: Introduction: Origins of paper and Survey 1 analysis</li> <li>[0:20:15] Interview: Results of survey 1 and ethical research practices at Spotify</li> <li>[0:27:03] Interview: Second Survey construction, analysis, and results</li> <li>[0:34:37] Interview: Problems of terminology and labeling</li> <li>[0:43:27] Interview: Overall results and absence of vocals terms in music descriptions</li> <li>[0:53:30] Interview: Implications for everyday music listening</li> <li>[0:58:40] Closing with Brian (12/10 for efficient summary)</li> </ul> <h4>Show notes</h4> <ul> <li>Recommended article: <ul> <li>Demetriou, A., Jansson, A., Kumar, A., &amp; Bittner, R. M. <a href="http://ismir2018.ircam.fr/doc/pdfs/98_Paper.pdf" rel="nofollow" target="_blank">Vocals in Music Matter: The Relevance of Vocals in the Minds of Listeners</a>. Proceedings of ISMIR 2018 (pp. 514-520). </li> <li><a href="https://docs.google.com/presentation/d/1SZ67WD8_gvjUP3gMlkBm9B2tJdDIGnsrWl0b6itPEMk/" rel="nofollow" target="_blank">Slide deck</a> from the corresponding ISMIR talk that caught Brian’s attention</li> </ul> </li> <li>Interviewee: <a href="https://www.researchgate.net/profile/Andrew_Demetriou" rel="nofollow" target="_blank">Andrew Demetriou</a></li> <li>Co-host: <a href="https://bmcfee.github.io/" rel="nofollow" target="_blank">Prof. Brian McFee</a></li> </ul> And here is the action shot of the research team card sorting participants’ text responses to Survey 1.Spotify researchers (left to right) Rachel Bittner, Andreas Jansson, Andrew Demetriou, and Aparna Kumar working through the text responses to Survey 1. <h4>Credits</h4> The So Strangely Podcast is produced by Finn Upham, 2018. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.

Episode 6: Relevance of vocals to music listener preferences, with Brian McFee and guest Andrew Demetriou