Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Predominant Instrument Recognition in PolyphonicMusic Using Convolutional Recurrent Neural Networks
Lekshmi. C.R and Rajeev Rajan
College of Engineering Trivandrum
APJ Abdul Kalam Technological University
clekshmir04@gmail.com,rajeev@cet.ac.in

Abstract. In this paper, predominant instrument recognition in polyphonic music is addressed using convolutional recurrent neural networks (CRNN) throughMel-spectrogram, modgdgram, and its fusion. Modgdgram, a visual representation is obtained by stacking modified group delay functions of consecutive
frames successively. Convolutional neural networks (CNN) learn the distinctive
local characteristics from the visual representation and recurrent neural networks(RNN) integrate the extracted features over time and classify the instrument to thegroup where it belongs. The proposed system is systematically evaluated using
the IRMAS dataset. A wave generative adversarial network (WaveGAN) architecture is also employed to generate audio files for data augmentation. We experimented with two CRNN architectures, convolutional long short-term memory(C-LSTM) and convolutional gated recurring unit (C-GRU). The fusion experiment C-GRU reports a micro and macro F1 score of 0.69 and 0.60, respectively.
These metrics are 7.81% and 9.09% higher than those obtained by the state-ofthe-art Han’s model. The architectural choice of CRNN with score-level fusion on
Mel-spectro/modgd-gram has merit in recognizing the predominant instrument inpolyphonic music.
Keywords: predominant, Mel-spectrogram, modgdgram,convolutional gated recurring unit.

1

Introduction

Predominant instrument recognition refers to the problem where the prominent instrument is identified from a mixture of instruments being played together [16]. In polyphonic music, the interference of simultaneously occurring sounds makes instrument
recognition harder. Automatic identification of lead instrument is important since theperformance of the source separation can be improved significantly by knowing thetype of the instrument [16].
Han et al. [16] employed Mel-spectrogram-CNN approach for instrument recognition. Pons et al. [22] analyzed the architecture of Han et al. in order to formulate
an efficient design strategy to capture the relevant information about timbre. Detecting the activity of music instruments using a deep neural network (DNN) through atemporal max-pooling aggregation is addressed in [15]. Dongyan et al. [31] employeda network with an auxiliary classification scheme to learn the instrument categories

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Fig. 1. Block diagram of proposed method of predominant instrument recognition.

through multitasking learning. Gomez et al. [14] investigated the role of two source
separation algorithms as pre-processing steps to improve the performance in the context of predominant instrument detection tasks. In [18], the Hilbert-Huang transform(HHT) is employed to map one-dimensional audio data into two-dimensional matrix
format, followed by CNN to learn the effective features for the task. In [17] an ensemble of VGG-like CNN classifiers is trained on non-augmented, pitch-synchronized,
tempo-synchronized, and genre-similar excerpts of IRMAS for the proposed task.
The modified group delay feature (MODGDF) is proposed for pitched musical instrument recognition in an isolated environment in [9]. While the commonly appliedmel frequency cepstral coefficients (MFCC) feature is capable of modeling the resonances introduced by the filter of the instrument body, it neglects the spectral characteristics of the vibrating source, which also, play its role in human perception of musicalsounds and genre classification [12]. Incorporating phase information is an effective attempt to preserve this neglected component. Some preliminary works on predominant
instrument recognition in polyphonic music using group delay functions are discussed
in [2, 1]. In [28] a multi-head attention mechanism is employed along with modified
group delay functions for proposed task.
In the proposed task, CRNN architecture with score level fusion of Mel-spectrogramand modgdgram is used for recognizing predominant instruments in polyphonic music.
Similar approaches combining CNNs and RNNs have been presented recently in manymusic processing applications [6], [5], [20]. The idea of including modified group delay
functions and GAN-based data augmentation strategy are the main contributions of the
proposed scheme.
Section 2 explains the system description. Feature extraction is described in Section
3, followed by the model architectures in Section 4. The performance evaluation is186Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Table 1. Model summary of CNN and CRNN architectures.(* represents the multiplication factor,
di ,fi ,hi ,ji represents the number of filters used in the networks. (di =8,16,24,32,64,128,256,512),(fi =32,64,128,256), (hi =8,16,32,64,128,256), (ji =32,64,128).

Mel-spectrogram- ModgdgramMel-spectrogram- ModgdgramCNN
CNN
CRNN
CRNN
*
*
Conv2D
Conv2Dx4 2 X Conv2D
x3 2 X Conv2D(3x3) , di(3x3), fi(3x3) , hi(3x3), ji
Leaky ReLU
ReLU
Leaky ReLU
ReLU
( α = 0.33)( α = 0.33)
3x3 Max-pooling,stride (3,3)
Batch Normalization
Dropout (0.25)
2x2 Max-pooling,stride(2,2)
Global Max-pooling
Flatten (1024)
Dense (1024)
Dense(512)2 X Bidirectional
LSTM / GRU (32 units)
Dropout (0.5)
Flatten (1024)
Dense (11), Softmax Activation
Dense (512)
Batch Normalization, Dropout (0.5)
Dense (11), Softmax Activation

described in Section 5. The results are analyzed in Section 6. The paper is concluded in
Section 7.

2

System Description

The proposed scheme is shown in Fig. 1. In the proposed model, CRNN is used to learn
the distinctive characteristics from Mel-spectro/modgd-gram to identify the leading instrument in a polyphonic context. We evaluate the proposed method on the IRMAS
dataset and compare its performance to CNN and two variants of RNN-long short-term
memory (LSTM) and gated recurring unit (GRU). The performance is also comparedwith a DNN framework. As a part of data augmentation, additional training files are
generated using WaveGAN. During the testing phase, the probability value at the output nodes of the trained model is treated as the score corresponding to the input test
file. The input audio file is classified to the node which gives the maximum score during testing. In the fusion framework, the individual scores of Mel-spectro/modgd-gramexperiments are fused at the score-level to make a decision. The fusion score Sf , isobtained by,
Sf = βSspectro + (1 − β)Smodgd(1)

where Sspectro , Smodgd , β are the Mel-spectrogram score, modgdgram score and weighting constant, respectively. The value of β has been empirically chosen to be 0.5. Each
phase is explained in detail in the following sections.

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3

Feature Extraction

Mel-spectrogram and modgdgram are the inputs used in the proposed scheme. Melspectrogram approximates how the human auditory system works and can be seen as
the spectrogram smoothed, with high precision in the low frequencies and low precisionin the high frequencies [21]. It is computed with a frame size of 50 ms and a hop sizeof 10 ms with 128 bins for the given task.
Group delay features are being employed in numerous speech and music processing applications [24, 26, 23, 25]. The group delay function is defined as the negative
derivative of the unwrapped Fourier transform phase with respect to frequency. Modified group delay functions (MODGD), τm (ejω ) are obtained by,τc (ejω ))(|τc (ejω )|)a ,|τc (ejω )|(2)

XR (ejω )YR (ejω ) + YI (ejω )XI (ejω ).
|S(ejω )|2b(3)

τm (ejω ) = (where,τc (ejω ) =The subscripts R and I denote the real and imaginary parts, respectively. X(ejω ),Y (ejω ) and S(ejω ) are the Fourier transforms of signal, x[n], n.x[n] ((weighted signal
with index), and the cepstrally smoothed version of X(ejω ), respectively. a and b (0 <a, b ≤ 1 ) are introduced to control the dynamic range of MODGD [19, 23]. Modgdgram
is the visual representation of MODGD with time and frequency in the horizontal andvertical axis, respectively. The amplitude of group delay function at a particular time isrepresented by the intensity or color in the third dimension. Modgdgrams are computedwith a frame size of 50 ms and hop size of 10 ms using a and b values of 0.9 and 0.5
respectively.

4

Model Architectures

CNNs and RNNs are specific instances of the CRNN architecture presented in this section: A CNN is a CRNN with zero recurrent layers, and an RNN is a CRNN with zeroconvolutional layers. CNN uses a deep architecture similar to [16] with repeated convolution layers followed by max-pooling. The detailed architecture for Mel-spectrogramand modgdgram CNN and CRNN are shown in Table 1.
RNNs are introduced to handle sequence and time-series data and are well suitedfor various speech and music-related applications [27], [13]. RNN with sophisticatedrecurrent hidden units like LSTM and GRU is used because such structures are capableof alleviating the vanishing gradient problem. The designed RNN consists of one input
layer, two hidden layers which include two LSTM or GRU layers each with 32 nodes,
and an output dense layer with eleven nodes for output classes. ReLU activation is used
for hidden layers and softmax is used for the output layer.
In order to benefit from both approaches, the two architectures can be combined intoa single network with convolutional layers followed by recurrent layers, often referred
to as CRNN. The CRNN makes use of the CNN architecture for the task of feature extraction while using LSTM and GRU placed at the end of the architecture to summarise

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the temporal information of the extracted features. The main drawback of CNNs is it
lacks longer temporal context information. However, RNNs do not easily capture theinvariance in the frequency domain, rendering high-level modeling of the data more difficult [5]. In the C-LSTM and C-GRU architectures, batch normalization is employed
after convolutional layers to improve the training speed and performance. Two bidirectional LSTM/GRU units are connected after the time-distributed flatten layer. The
bidirectional RNN is preferred rather than unidirectional RNN since it considers the
future timestamp representations also [8]. The CNN and CRNN networks are trainedusing Adam optimizer with a learning rate of 0.001.

Fig. 2. Visual representation of an audio excerpt with acoustic guitar as leading, Mel-spectrogramof original and WaveGAN-generated (Upper pane left and right). Modgdgram of original and
WaveGAN-generated (Lower pane left and right).

A DNN framework on musical texture features (MTF) is also experimented with to
examine the performance of deep learning methodology on handcrafted features. MTF
includes MFCC-13 dim, spectral centroid, spectral bandwidth, root mean square energy, spectral roll-off, and chroma STFT. The features are computed with a frame sizeof 40 ms and a hop size of 10 ms using Librosa framework 1 . DNN consists of sevenlayers, with increasing units from 8 to 512. ReLU has been chosen for hidden layers
1

https://librosa.org/doc/latest/tutorial.html

189Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

and softmax for the output layer. The network is trained using categorical cross-entropy
loss function for 500 epochs using Adam optimizer with a learning rate of 0.001.

5

Performance Evaluation

5.1

DatasetIRMAS dataset [11], comprising eleven classes, is used for the evaluation. The classes
include cello (Cel), clarinet (Cla), flute (Flu), acoustic guitar (Gac), electric guitar(Gel), organ (Org), piano (Pia), saxophone (Sax), trumpet (Tru), violin (Vio) and humansinging voice (Voice). The training data are single-labeled and consists of 6705 audio
files with excerpts of 3 s from more than 2000 distinct recordings. On the other hand,
the testing data are multi-labeled and consist of 2874 audio files with lengths between
5 s and 20 s and contain the presence of multiple predominant instruments.
5.2

Data Augmentation using WaveGAN

WaveGAN v2 is used here to generate polyphonic files with the leading instrument required for training. WaveGAN is similar to DCGAN, which is used for Mel-spectrogram
generation, in various music processing applications. The transposed convolution operation of DCGAN is modified to widen its receptive field in WaveGAN. For training, the
WaveGAN optimizes WGAN-GP using Adam for both generator and discriminator.
A constant learning rate of 0.0001 is used with β1 = 0.5 and β2 = 0.9 [10]. WaveGAN is trained for 2000 epochs on the three sec audio files of each class to generate
similar audio files and a total of 6585 audio files with cello (625), clarinet (482), flute(433), acoustic guitar (594), electric guitar (732), organ (657), piano (698), saxophone(597), trumpet (521), violin (526) and voice (720) are generated. The generated files aredenoted by T raing and training files available in the corpus are denoted by T raind .
Mel-spectrogram and modgdgram of natural and generated audio files for acoustic guitar are shown in Fig. 2. The experiment details and a few audio files can be accessed athttps://sites.google.com/view/audiosamples-2020/home/instrument
The quality of generated files is evaluated using a perception test. It is conductedwith ten listeners to assess the quality of generated files for 275 files covering all classes.
Listeners are asked to grade the quality by choosing one among the five opinion gradesvarying from poor to excellent quality (scores, 1 to 5). A mean opinion score of 3.64
is obtained. This value is comparable to the mos score obtained in [10] and [3] using
WaveGAN.
5.3

Experimental Set-up

The experiment is progressed in three phases namely Mel-spectrogram-based, modgdgram-based, and score-level fusion-based. Han’s sliding window baseline model [16] isimplemented for the given experiment with 1 s slice length for performance comparison 2 . We used the same aggregation strategy (S2) as that of Han’s model, by summing2

https://github.com/Veleslavia/EUSIPCO2017

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Table 2. F1 score for the experiments with data augmentation (T raind + T raing ).

SL. Class MTF
No
DNN

Han’s Fusion
Model CNN

FusionLSTM

FusionGRU

FusionFusionC-LSTM C-GRU

F1

F1

F1

F1

F1

F1

F1

0.15
0.26
0.27
0.43
0.36
0.28
0.36
0.28
0.18
0.22
0.32

0.55
0.18
0.43
0.72
0.69
0.45
0.67
0.61
0.44
0.48
0.85

0.55
0.36
0.55
0.63
0.67
0.55
0.62
0.58
0.65
0.68
0.73

0.15
0.13
0.32
0.44
0.50
0.37
0.50
0.25
0.33
0.38
0.60

0.36
0.36
0.62
0.54
0.49
0.49
0.57
0.55
0.62
0.49
0.58

0.42
0.48
0.34
0.51
0.62
0.66
0.78
0.47
0.43
0.64
0.85

0.50
0.39
0.31
0.70
0.74
0.51
0.78
0.50
0.60
0.69
0.88

Macro 0.28
Micro 0.32

0.55
0.64

0.60
0.65

0.36
0.43

0.52
0.55

0.56
0.65

0.60
0.69

1 Cel
2 Cla
3 Flu
4 Gac
5 Gel
6 Org
7 Pia
8 Sax
9 Tru
10 Vio
11 Voice

all the softmax predictions followed by normalization and applying a threshold of 0.5.
Mel-spectrograms and modgdgrams of input size 128x100x1, corresponding to a window size of 1 s are applied to the corresponding network. The experiments are repeatedfor CNN, RNN with LSTM and GRU, CRNN with C-LSTM, and C-GRU respectively.
Since the number of annotations for each class was not equal, we computed precision,
recall, and F1 measures for both the micro and the macro averages. For the micro averages, we calculated the metrics globally, thus giving more weight to the instrumentwith a higher number of appearances. On the other hand, we calculated the metrics foreach label and found their unweighted average for the macro averages.

6

Results and Analysis

Several studies [30, 29] have demonstrated that by consolidating information from multiple sources, better performance can be achieved than uni-modal systems which motivated us to perform the score-level fusion. The standard metrics for various algorithms
on the IRMAS corpus are reported in Table 3. Fusion network C-GRU achieved micro and macro F1 measures of 0.69 and 0.60, respectively, which is 7.81% and 9.09%
higher than those obtained for the state-of-the-art Han’s model. Han employed Melspectrogram-CNN for the proposed task. Conventionally, the spectrum-related features
used in instrument recognition take into account merely the magnitude information.
However, there is often additional information concealed in the phase, which could be
beneficial for recognition [9]. The experimental results validate the claim in [9]. Our
Fusion-CNN with data augmentation reports a micro and macro F1 score of 0.65 and0.60 respectively which is 1.56% and 5.26% higher than that obtained for our Mel-191

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Table 3. Performance comparison on IRMAS dataset

SL.No Model
F1 Micro F1 Macro
1 Bosch et al. [4]
0.50
0.43
2 Han et al. [16]
0.65
0.50
3 Pons et al. [22]
0.65
0.52
4 Kratimenos et al. [17]
0.65
0.55
5 MTF-DNN (T raind + T raing )
0.32
0.28
6 Han Model (T raind + T raing )
0.64
0.55
7 Proposed Mel-spectrogram-CNN (T raind + T raing )
0.64
0.57
8 Proposed Modgdgram-CNN (T raind + T raing )
0.54
0.53
9 Proposed Fusion-CNN (T raind + T raing )
0.65
0.60
10 Proposed Fusion-C-LSTM (T raind + T raing )
0.65
0.56
11 Proposed Fusion-C-GRU (T raind )
0.62
0.53
12 Proposed Mel-spectrogram-C-GRU (T raind + T raing ) 0.66
0.59
13 Proposed Modgdgram-CGRU (T raind + T raing )
0.55
0.53
14 Proposed Fusion-C-GRU (T raind + T raing )
0.69
0.60

spectrogram-CNN with data augmentation. It is evident that modgdgram added complementary information to the spectrogram approach and the importance of the fusion
framework for the proposed task. Han’s model and the proposed Mel-spectrogram-CNN
approach show similar performance with better performance for the proposed architectural choice.
The F1 score of different fusion experiments is tabulated in Table 2. Fusion experiments using RNNs alone do not show improved performance over existing algorithms,
however, GRU shows better performance than LSTM. Since we employed the same
number of hidden units for both, GRU required less number of trainable parametersand makes faster progress, and reaches the convergence earlier than LSTM. Fusion experiments C-LSTM and CNN show similar performance, but C-GRU outperforms all
the models. GRUs train faster and computationally more efficient than LSTM becauseof fewer trainable parameters. Results of the experiments described in [7] suggest that
GRUs perform better than LSTMs on small polyphonic dataset [7]. Our C-LSTM for
Mel-spectrogram requires 100224 more trainable parameters compared to C-GRU. It
reaches convergence faster without compromising accuracy. The experimental results
validate the claim in [7].
Our best model Fusion C-GRU, without data augmentation (T raind ) reports micro
and macro F1 score of 0.62 and 0.53 respectively. Fusion C-GRU (T raind + T raing )reports micro and macro F1 scores of 0.69 and 0.60, respectively, with an improvementof 11.29% and 13.21% higher than that obtained by Fusion C-GRU (T raind ). Thisshows the significance of data augmentation in the proposed task.
Our proposed CRNN technique outperformed existing algorithms on the IRMAS
dataset for both the micro and the macro F1 measures. The analysis of the experimental
frameworks shows the significance of CRNN architecture for the proposed task. Be-

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sides, the experiments show the potential of fusion of magnitude and phase informationin the proposed task.

7

Conclusion

We presented a CRNN-based predominant instrument recognition system using Melspectro/modgd-gram. CRNN is used to capture the instrument-specific characteristics
and then do further classification. The proposed method is evaluated on IRMAS dataset.
Data augmentation is also performed using WaveGAN. The results show the potentialof C-GRU architecture on the score-level fusion of Mel-spectrogram and modgdgram
in the proposed task.

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