Acquiring New Musical Grammars: a Statistical Learning Approach

نویسندگان

  • Psyche Loui
  • David Wessel
  • Carla Hudson Kam
چکیده

In the present study we examine the ability of humans to acquire knowledge via passive exposure to a new musical system. We designed two new musical grammars based on a non-Western tuning system, and created melodies as legal exemplars of each grammar. In two experiments each participant was exposed to a set of melodies from one grammar. Several tests were conducted to assess learning, including forced-choice recognition and generalization, preand post-exposure probe tone ratings, and subjective preference ratings. In Experiment 1, five melodies were presented repeatedly. Participants correctly recognized and preferred melodies they had heard, but failed to generalize their recognition to new exemplars of the same grammar. In Experiment 2, 15 melodies were presented repeatedly. Participants showed some tendency to make generalizations about new melodies in their given grammar, and also showed an increased sensitivity to the statistics of the musical grammar following exposure. Results suggest that larger sets of exemplars promote the extraction of regularities underlying the examples, whereas smaller sets lead to better recognition and are more likely to influence subjective preference.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Statistical learning of speech, not music, in congenital amusia.

The acquisition of both speech and music uses general principles: learners extract statistical regularities present in the environment. Yet, individuals who suffer from congenital amusia (commonly called tone-deafness) have experienced lifelong difficulties in acquiring basic musical skills, while their language abilities appear essentially intact. One possible account for this dissociation bet...

متن کامل

Generalizing Transduction Grammars to Model Continuous Valued Musical Events

We describe a generalization of stochastic transduction grammars to be able to model continuous values, the first models to natively handle continuous-valued musical events such as microtones while still gaining the advantages of STGs for describing complex structural, hierarchically compositional inter-part relationships. Music transduction modeling based on linguistic or grammatical models ha...

متن کامل

Perceptual and memory constraints on language acquisition.

A wide variety of organisms employ specialized mechanisms to cope with the demands of their environment. We suggest that the same is true for humans when acquiring artificial grammars, and at least some basic properties of natural grammars. We show that two basic mechanisms can explain many results in artificial grammar learning experiments, and different linguistic regularities ranging from st...

متن کامل

How Blue Can You Get? Learning Structural Relationships for Microtones via Continuous Stochastic Transduction Grammars

We describe a new approach to probabilistic modeling of structural inter-part relationships between continuous-valued musical events such as microtones, through a novel class of continuous stochastic transduction grammars. Linguistic and grammar oriented models for music commonly approximate features like pitch using discrete symbols to represent ‘clean’ notes on scales. In many musical genres,...

متن کامل

Simultaneous Unsupervised Learning of Flamenco Metrical Structure, Hypermetrical Structure, and Multipart Structural Relations

We show how a new unsupervised approach to learning musical relationships can exploit Bayesian MAP induction of stochastic transduction grammars to overcome the challenges of learning complex relationships between multiple rhythmic parts that previously lay outside the scope of general computational approaches to music structure learning. A good illustrative genre is flamenco, which employs not...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006