The So Strangely Podcast Finn Upham

Music technology PhD Candidate Tim de Reuse recommends “Unmixer: An Interface for Extracting and Remixing Loops” by Jordan Smith,Yuta Kawasaki, and Masataka Goto, published in the proceedings of ISMIR 2019. Tim and Finn interview Jordan about the origins of this project, the algorithm behind the loop extraction, the importance of repetition in music, and the creative and playful applications of Unmixer.







Note: This conversation was recorded in December 2019. Techically issues with some tracks contributed to delays. Apologies for the choppy audio quality.







Time Stamps









* [0:01:40] Project Summary







* [0:05:05] Demonstration of Unmixer







* [0:14:27] Origins of the UnMixer project 







* [0:19:44] Factorisation algorithm 







* [0:28:37] Computational and musical objectives for factorisation







* [0:36:15] The Unmixer web interface







* [0:41:30] 2nd Demonstration, parameters and track selection







* [0:49:13] What Unmixer tells us about music









Show notes









* Recommended article:



* Smith, J, Kawasaki, Y, & Goto, M. (2019) Unmixer: An Interface for Extracting and Remixing Loops. Proceedings of  20th ISMIR meeting, Delft Netherlands.







* UnMixer website: https://unmixer.ongaaccel.jp/







* Project webpage











* Interviewee: Dr. Jordan BL Smith, Research Scientist at Tik Tok.Website, twitter







* Co-host: PhD Candidate Tim de Reuse, website, twitter







* Papers cited in the discussion:



* Smith, J. B., & Goto, M. (2018, April). Nonnegative tensor factorization for source separation of loops in audio. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 171-175). IEEE.







* Schmidhuber, J. (2009). Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. Journal of SICE, 48(1).







* Rafii, Z., & Pardo, B. (2012). Repeating pattern extraction technique (REPET): A simple method for music/voice separation. IEEE transactions on audio, speech, and language processing, 21(1), 73-84.











* Music sampled:



* Daft Punk, Random Access Memories (2013): Doing it Right (ft. Panda Bear)







* Martin Solveig & Dragonette, Smash (2011): Hello - Single Edit







* Mura Masa, Soundtrack To a Death (2014): I've Never Felt So Good











* Other references:



* Madeon's Adventure Machine

In western classical music, theorists have long argued (and mostly agreed) that individual notes of the major and minor scale have sensations associated, feelings often described in terms of tension, motion, sadness, and stability. Dr Baker recommends Prof. Clair Arthur’s paper “A perceptual study of scale-degree qualia in context” from Music Perception (2018) which describes testing these associations through the subjective reports of musicians and non-musicians when presented scale degrees in different harmonic contexts. Together we discuss the challenges of the probe tone paradigm, interactions of musicianship training and perception of tonality, and ambiguity in note qualia perception.

Time Stamps



[0:00:10] Introductions

[0:02:40] Summary of Paper

[0:09:50] Origins and Experiment 1 - free association

[0:16:57] Experiment 2 - probe tone ratings

[0:23:25] Results and surprises

[0:28:59] Inconsistency in qualia reports

[0:34:20] Stimulus examples and experiment limitations

[0:41:21] Implications of findings

[0:50:43] Using Musically trained participants

[0:53:51] Closing summary



Show notes



Recommended article:



Arthur, C. (2018). A perceptual study of scale-degree qualia in context. Music Perception: An Interdisciplinary Journal, 35(3), 295-314





Interviewee: Prof. Claire Arthur of Georgia Tech University 

Co-host: Dr. David Baker, Lead Instructor of Data Science at the Flatiron School 

David Huron’s Sweet Anticipation, 2006 from MIT Press



Credits

The So Strangely Podcast is produced by Finn Upham, 2020. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.

This episode brings recommendations from the 2019 ISMIR conference at TUDelft in the Netherlands. A number of contributors, old and new, highlighted papers that had caught their attention. 







Note: At ISMIR, all accepted papers were presented via a short 4 minute talk and a poster. This arrangement made it possible to keep all presentations in a single track. All papers and permited talks are posted on the ISMIR site.







Time Stamps









* [0:01:51] Matan’s rec







* [0:07:27] Rachel’s rec







* [0:10:51] Andrew’s rec







* [0:15:20] Ashley and Felicia’s rec







* [0:19:59] Néstor’s rec







* [0:26:55] Tejaswinee’s rec







* [0:31:13] Brian’s rec







* [0:36:06] Finn’s recs









Show notes









* Matan Gover recommends [A13] Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations by Gabriel Meseguer Brocal and Geoffroy Peeters (paper, presentation)







* Andrew Demetriou recommends [F10] Tunes Together: Perception and Experience of Collaborative Playlists by So Yeon Park; Audrey Laplante; Jin Ha Lee; Blair Kaneshiro (paper, presentation)







* Tejaswinee Kelkar recommends [B03] Estimating Unobserved Audio Features for Target-Based Orchestration by Jon Gillick; Carmine-Emanuele Cella; David Bamman (paper, presentation)







* Ashley Burgoyne and Felicia Villalobos recommend [E13] SAMBASET: A Dataset of Historical Samba de Enredo Recordings for Computational Music Analysis by Lucas Maia; Magdalena Fuentes; Luiz Biscainho; Martín Rocamora; Slim Essid (paper, presentation)







* Néstor Nápoles López recommends the anniversary paper [E-00] 20 Years of Automatic Chord Recognition from Audio by Johan Pauwels; Ken O'Hanlon; Emilia Gomez; Mark B. Sandler (paper, presentation)







* Rachel Bittner recommends [A06] Cover Detection with Dominant Melody Embeddings by Guillaume Doras; Geoffroy Peeters (paper, presentation)







* Brian McFee recommends [E-06] FMP Notebooks: Educational Material for Teaching and Learning Fundamentals of Music Processing by Meinard Müller; Frank Zalkow (paper, a href="https://collegerama.

Finn interviews Composer and Machine Learning specialist Dr. Cheng-Zhi Anna Huang about the Music Transformer project at Google’s Magenta Labs. They discuss representations of music for machine learning, algorithmic music generation as a compositional aid, the JS Bach Google Doodle, how self-reference defines structure in music, and compare the musicality of different systems with example outputs.







Time Stamps









* [0:01:05] Introducing Dr. Anna Huang







* [0:03:43] JS Bach Google Doodle







* [0:12:52] Representations of musical information for machine learning







* [0:16:26] Music Transformer project



* [0:25:15] RNN algorithm music sample











* [0:25:45] ABA structure challenge for generative systems



* [0:30:30] Vanilla Transformer algorithm music sample 







* [0:32:07] Music Transformer algorithm music sample 











* [0:36:30] Self Reference Visualisation (see blog post)







* [0:43:27] Everyday music implications







* [0:48:10] What this work says about music



* [0:50:01] Music Transformer trained on Jazz Piano 













Show notes









* Recommended project:



* Blog post: Huang, C.Z.A., Simon, I., & Dinculescu, M. (2018, Dec 12). Music Transformer: Generating Music with Long-Term Structure [Blog Post]







* Paper: Huang, C.Z.A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Dai, A.M., Hoffman, M.D., Dinculescu, M., & Eck, D. (2018) MUSIC TRANSFORMER: GENERATING MUSIC WITH LONG-TERM STRUCTURE on arXiv.org











* Interviewee:  Dr. Cheng-Zhi Huang at Google AI, on twitter @huangcza







* Google Doodle Celebrating JS Bach with AI harmonising melodies







* Related papers:



* Huang, C.Z.A., Cooijmans, T., Roberts, A., Courville, A., Eck, D. (2017). Coconet: Counterpoint by Convolution. ISMIR.







* Huang, C.Z.A., Cooijmans, T., Dinculescu, M., Roberts, A., & Hawthorne, C. (2019, Mar 20). Coconet: the ML model behind today’s Bach Doodle.







* Huang, C.Z.A., Hawthorne, C., Roberts, A., Dinculescu, M., Wexler, J., Hong, L., Howcroft, J. (2019). The Bach Doodle: Approachable music composition with machine learning at scale. ISMIR.













Credits







The So Strangely Podcast is produced by Finn Upham, 2018. Algorithmic music samples from the blog post Music Transformer: Generating Music with Long-Term Structure, and included under the principles of fair dealing. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.

Music Education doctoral candidate Ethan Hein recommends “Equity and Music Education: Euphemisms, Terminal Naivety, and Whiteness” by Juliet Hess, published in Action, Criticism & Theory for Music Education, 2017. Ethan and Finn interview Dr. Juliet Hess about this study and whiteness in music education, and addressing systemic racism from within our areas of academia.

Time Stamps



[0:00:10] Intro with Ethan Hein

[0:08:29] Interview: Dr. Juliet Hess, Background and Case Studies

[0:18:50] Interview: Multiculturalism and Music

[0:29:31] Interview: Whiteness in the Conservatory

[0:36:19] Interview: Context and Implications

[0:44:06] Interview: Future work

[0:51:50] Closing with Ethan Hein



Show notes



Recommended article:



Hess, J. (2017). Equity and Music Education: Euphemisms, Terminal Naivety, and Whiteness. Action, Criticism & Theory for Music Education, 16(3). (HTML, PDF)





Interviewee: Dr. Juliet Hess, Assistant Professor of Music Education at Michigan State University 

Co-host: Ethan Hein, Doctoral Candidate in Music Education at New York University (website, twitter)

Sources cited in the discussion:



Kendrick Lamar's Alright (youtube)

Chris Thile's performance on Prairie Home companion is no longer available

Emma Stevens - Blackbird by The Beatles sung in Mi'kmaq (youtube)



Correction: this performance is from Cape Breton, Nova Scotia, not Newfoundland where there has been controversy around seal hunting. Both provinces are within the ancestral territory of Mi'kmaq People.





Bonilla-Silva, Eduardo. 2006. Racism without racists: Color-blind racism and the persistence of racial inequality in the United States. 2nd edition. Toronto, ON: Rowman & Littlefield Publishers, Inc. (Publisher page)

Juliet Hess (2018) Interrupting the symphony: unpacking the importance placed on classical concert experiences, Music Education Research, 20:1, 11-21, DOI: 10.1080/14613808.2016.1202224 (HTML)

Juliet Hess’ new book: 



Hess, Juliet. (2019) Music Education for Social Change: Constructing an Activist Music Education,  Routledge (Publisher page)











Credits

The So Strangely Podcast is produced by Finn Upham, 2019. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.

Host Finn Upham recommends “How Music Moves Us: Entraining to Musicians’ Movements” by Alexander Demos and Roger Chaffin, published in Music Perception, 2017. They interview Dr Demos about this study and adjacent issues.

Note: This interview goes fairly deep into the challenges of time series data analysis. Feel free to use the time stamps listed in the show notes to skip ahead if this is not your cup of tea.

Time Stamps



* [0:00:10] Intro to article and Alex

* [0:03:20] Design of Air Conducting experiment

* [0:11:15] Capturing movements of performers and listeners

* [0:15:40] Assessing alignment between motion time series

* [0:25:26] Non-linearity in these time series

* [0:31:18] False negatives and intermittent alignment

* [0:38:32] Theories of Music and Ancillary motion

* [0:45:04] Closing Summary and Implications  



Show notes



Recommended article:



Demos, A. P., & Chaffin, R. (2018). How Music Moves Us: Entraining to Musicians’ Movements. Music Perception: An Interdisciplinary Journal, 35(4), 405-424.  (pdf)





Interviewee: Dr. Alexander Demos, Clinical assistant professor at the University of Illinois at Chicago (website)

Some publications cited in the discussion:



Schreiber, T., & Schmitz, A. (1996). Improved surrogate data for nonlinearity tests. Physical Review Letters, 77(4), 635–638.

Cook, N. (2013). Beyond the score: Music as performance. Oxford University Press.

Theiler, J., Eubank, S., Longtin, A., Galdrikian, B. & Farmer, J. D. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica D, 58, 77–94.

Dean, R. T., Bailes, F., & Dunsmuir, W. T. (2014). Time series analysis of real-time music perception: Approaches to the assessment of individual and expertise differences in perception of expressed affect. Journal of Mathematics and Music, 8(3), 183-205.

Wanderley, M. M., Vines, B. W., Middleton, N., Mckay, C., & Hatch, W. (2005). The musical significance of clarinetists’ ancillary gestures: An exploration of the field. Journal of New Music Research, 34(1), 97–113. DOI: 10.1080/092982105 00124208







Credits

The So Strangely Podcast is produced by Finn Upham, 2019. The closing music includes a sample of Diana Deutsch’s Speech-Song Illusion sound demo 1.