1 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI LANGUAGE AND INTELLIGENCE U N I V E R S I T Y O F P I S A DEPARTMENT OF COMPUTER SCIENCE Automatic Transcription of Piano Music Sara Corfini

2 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI INTRODUCTION Trascribing recordings of piano music into a MIDI rapresentation  MIDI provides a compact representation of musical data  Score-following for computer-human interactive performance  “Signal-to-score” problem A hidden Markov model approach to piano music transcription  A “state of nature” can be realized through a wide range of data configurations  Probabilistic data representation  Automatically learning this probabilistic relationship is more flexible than optimizing a particular model  Rules describing the musical structure can be more accurately represented as tendencies

3 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE MODEL The acustic signal is segmented into a sequence of frames (“snapshots” of sound) For each frame a feature vector y 1,…,y N is computed Goal  to assign a label to each frame describing its content A generative probabilistic framework (a hidden Markov model) outputs  the observed sequence of features vectors y 1,…,y N hidden variables  labels A Hidden Markov model is composed of two processes X = X 1,…,X N and Y = Y 1,…,Y N X is the hidden (or label) process and describes the way a sequence of frame labels can evolve (a Markov chain) We do not observe the X process directly, but rather the feature vector data The likelihood of a given feature vector depends only on the corresponding label

4 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE LABEL PROCESS GOAL  to assign a label to each frame where each label ∈ L Components of the label  the pitch configuration (chord)  “attack”, “sustain”, “rest” portions of a chord We define a random process (a Markov chain) X 1,…,X N that takes value in the label set L The probability of the process occupying a certain state (label) in a given frame depends only on the preceding state (label) where p(x’|x) is the transition probability matrix and X 1 n = (X 1,…,X N )

5 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE LABEL PROCESS Markov model for a single chord Markov model for recognition problem  the final state of each chord model is connected to the initial state of each chord model  a silence model is constructed for the recorder space before and after the performance

6 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE OBSERVABLE PROCESS Rather than observe the label process x 1,…,x N, we observe feature vector data y 1,…,y N (probabilistically related to labels) Assumption of HMM  each visited state X n produces a feature vector Y n from a distribution that is characteristic of that state Hence, given X n, Y n, is conditionally independent of all other frame labels and all other feature vectors

7 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE OBSERVABLE PROCESS We compute a vector of features for each frame y = (y 1,…,y K ) The components of this vector are conditionally independent given that state The state are tied  different states share the same feature distributions Where the T k (x) is constructed by hand Hence we have

8 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE OBSERVABLE PROCESS T k (x) can be clarified by describing the computed features y 1  measures the total energy in the signal (to distinguish between the times when the pianist plays and when there is silence)  T 1 (x) = 0 for the silence and rest states  T 1 (x) = 1 for the remaining states  Two probabilistic distributions:  p(y k |T 1 (x)=0)  p(y k |T 1 (x)=1)  Partition of the label set generated by  T 1 (x) : {x ∈ L : T 1 (x)=0}, {x ∈ L : T 1 (x)=1}

9 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE OBSERVABLE PROCESS y 2  measures the local burstiness of the signal (to distinguish between note “attacks” and steady state behaviour) y 2 computes several measures of burstiness (is a vector) For this features, states can be partioned in three groups  T 2 (x) = 0  states at the beginning of each note (high burstiness)  T 2 (x) = 1  states corresponding to steady state behaviour (relatively low burstiness)  T 2 (x) = 2  silence states

10 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI THE OBSERVABLE PROCESS y 3,…,y K  concerns the problem of distinguishing between the many possible pitch configuration Each features of y 3,…,y K is computed from a small frequency interval of the Fourier transformed frame data For each window we compute  the empirical mean  location of the harmonic (when there is a single harmonic in the window)  the empirical variance  to distinguish probabilistically when there is a single harmonic (low variance) and when there is not (high variance) State can be partinioned as  T k (x) = 0  states in which no notes contain energy in the window  T k (x) = 1  states having several harmonics in the window  T k (x) = t  states having a single harmonic at approximately the same frequency in the window

11 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI TRAINING THE MODEL Since the HMM formulation, the probability distribution can be trained in an unsupervised fashion An iterative procedure (Baum-Welch algorithm) allows to automatically train from signal-score pairs When the score is known, we can build a model for the hidden process The algorithm  Starts from a neutral starting place (we begin with uniformly distributed output distributions)  Iterates the process of finding a probabilistic correspondence between model states and data frames  Next, we retrain the probability distribution using this corrispondence

12 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI TRAINING THE MODEL Output distributions on feature vectors are represented through decision trees For each distribution p(y k |T k (x)) we form a binary tree  Each non terminal node corresponds to a question y k,v < c (where y k,v is the v th component of feature k )  An observation y k can be associated with a non terminal node by dropping the observation down the tree (evaluating the root question)  The process continues until it arrives at a terminal node, denoted by Q k (y k ) As the training procedure evolves, the trees are re-estimated at each iteration to produce more informative probability distributions

13 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI RECOGNITION The traditional HMM approaches to recognitions seeks the most likely labeling of frames, given the data, through dynamic programming This corresponds to find the best path through the graph, where the reward in going from state x n-1 to x n in the n th iteration is given by The Viterbi algorithm constructs the optimal paths of lenght n from the optimal paths of length n-1 The computational complexity grows with the square of the state-space which is completely intractable in this case The state space is on the order of 10 8 (under restrictive assumptions on the possible collection of pitches and the number of notes in a chord)

14 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI RECOGNITION We use the data model constructed in the training phase to produce a condensed version of the state graph For each frame n we perform a greedy search that seeks a plausible collection of state x ∈ L for that frame This is accomplished by searching for states x giving large value to p(y n |x). The search is performed by  Finding the mostly likely 1 -note hypotheses  Then considering 2 -note hypotheses and so on Each frame n will be associated with a possible collection of states A n The state are blended by letting The graph is constructed by restricting the full graph to the B n set Disadvantage  if the true state at frame n is not captured by B n, then it cannot be recovered during recognition

15 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI EXPERIMENTS The hidden Markov model has been trained by using data taken from various Mozart piano sonatas The result concerns a performance of Sonata 18, K.570 Objective measure of performance  edit distance Recognition erroe rates are reported as Note error rate  39% (184 substitutions, 241 deletions, 108 insertions)  If two adjacent recognized chords have a pitch in common, it is assumed that the note is not rearticulated  Inability to distinguish between chord homonyms

16 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI CONCLUSION Recognition results leave room for improvements Results may be useful in a number of Music Information Retrieval applications tolerant of errorful representaions The current system works with no knowledge of the plausibility of various sequences of chord probability of chord sequence  Probabilistic model that models the likelihood of chord sequences The current system makes almost no effort to model the acoustic characteristics of the highly informative note onsets  A more sophisticated “attack” model would help in recognizing the many repeated notes which the system currently misses

17 AUTOMATIC TRANSCRIPTION OF PIANO MUSIC - SARA CORFINI REFERENCES Christopher Raphael Automatic transcription of piano music In Proceedings of the 3rd Annual International Symposium on Music Information Retrieval (ISMIR), Michael Fingerhut, Ed., pp , IRCAM - Centre Pompidou, Paris, France, October 2002.