3 research outputs found
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
A model for hit song prediction can be used in the pop music industry to
identify emerging trends and potential artists or songs before they are
marketed to the public. While most previous work formulates hit song prediction
as a regression or classification problem, we present in this paper a
convolutional neural network (CNN) model that treats it as a ranking problem.
Specifically, we use a commercial dataset with daily play-counts to train a
multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss
to learn from audio the relative ranking relations among songs. Besides, we
devise a number of pair sampling methods according to some empirical
observation of the data. Our experiment shows that the proposed model with a
sampling method called A/B sampling leads to much higher accuracy in hit song
prediction than the baseline regression model. Moreover, we can further improve
the accuracy by using a neural attention mechanism to extract the highlights of
songs and by using a separate CNN model to offer high-level features of songs
Music Popularity: Metrics, Characteristics, and Audio-Based Prediction
Understanding music popularity is important not only for the artists who
create and perform music but also for the music-related industry. It has not
been studied well how music popularity can be defined, what its characteristics
are, and whether it can be predicted, which are addressed in this paper. We
first define eight popularity metrics to cover multiple aspects of popularity.
Then, the analysis of each popularity metric is conducted with long-term
real-world chart data to deeply understand the characteristics of music
popularity in the real world. We also build classification models for
predicting popularity metrics using acoustic data. In particular, we focus on
evaluating features describing music complexity together with other
conventional acoustic features including MPEG-7 and Mel-frequency cepstral
coefficient (MFCC) features. The results show that, although room still exists
for improvement, it is feasible to predict the popularity metrics of a song
significantly better than random chance based on its audio signal, particularly
using both the complexity and MFCC features
Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops
Music producers who use loops may have access to thousands in loop libraries,
but finding ones that are compatible is a time-consuming process; we hope to
reduce this burden with automation. State-of-the-art systems for estimating
compatibility, such as AutoMashUpper, are mostly rule-based and could be
improved on with machine learning. To train a model, we need a large set of
loops with ground truth compatibility values. No such dataset exists, so we
extract loops from existing music to obtain positive examples of compatible
loops, and propose and compare various strategies for choosing negative
examples. For reproducibility, we curate data from the Free Music Archive.
Using this data, we investigate two types of model architectures for estimating
the compatibility of loops: one based on a Siamese network, and the other a
pure convolutional neural network (CNN). We conducted a user study in which
participants rated the quality of the combinations suggested by each model, and
found the CNN to outperform the Siamese network. Both model-based approaches
outperformed the rule-based one. We have opened source the code for building
the models and the dataset.Comment: Accepted to the 21st International Society for Music Information
Retrieval Conference (ISMIR 2020