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

Knowledge Transfer from Neural Networks for SpeechMusic Classification
Christian Kehling1,2 and Estefanı́a Cano3 
1Institute for Digital Media Technology
Technical Univeristy of Ilmenau
2
Fraunhofer Institute for Digital Media Technology
3
Songquito UG
christian.kehling@tu-ilmenau.de

Abstract. A frequent problem when dealing with audio classification tasks is
the scarcity of suitable training data. This work investigates ways of mitigating
this problem by applying transfer learning techniques to neural network architectures for several classification tasks from the field of Music Information Retrieval(MIR). First, three state-of-the-art architectures are trained and evaluated withseveral datasets for the task of speech/music classification. Second, feature representations or embeddings are extracted from the trained networks to classify newtasks with unseen data. The effect of pre-training with respect to the similarity ofthe source and target tasks are investigated in the context of transfer learning, as
well as different fine-tuning strategies.
Keywords: Deep Learning, Neural Networks, Audio Classification, Speech Music Classification, Transfer Learning, Embeddings, Music Information Retrieval

1

Introduction

Detection of speech and music in audio signals has been investigated in the field ofMusic Information Retrieval (MIR) to automatically enrich audio archives with metadata. In addition to binary classification where only one of the classes is assumed to
be present at time more complex tasks like segmentation of speech or music as wellas multi-label classification where multiple classes can be present at time gained popularity. Despite the vast amount of research in this field [23, 12, 14, 24, 13, 5, 20, 4, 8],
speech/music classification (SMC) remains challenging in the presence of noise, theinvolvement of chanting, or under low-quality recording conditions [15]. SMC was first
addressed with algorithms based on audio features (e.g., pitch, zero crossing rate) [23,14, 12]. Recent approaches almost entirely focus on deep neural networks (DNN) that
directly learn to detect desired audio properties from input signals and its corresponding annotations [13, 2, 5, 20]. In an attempt to make audio classifiers more robust tovarying signal conditions and data scarcity, pre-trained feature representations (embeddings) from related tasks are tranferred to new tasks, so called Transfer Learning (TL),
to avoid exhaustive training from scratch [3, 6, 8, 9, 2].
This work has been supported by the German Research Foundation (BR 1333/20-1,
CA 2096/1-1)

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This work is divided in two stages. First, we analyze three state-of-the-art neural network architectures for SMC and evaluate their robustness to varying signal conditions
by using a diversity of datasets. Here we aim to understand whether any of the threearchitectures is more robust to varying signal characteristics when trained under comparable conditions. In the second stage of our work, audio embeddings are computedfrom the three pre-trained architectures. These embeddings are then transferred to different MIR tasks. In this stage, we aim to understand how pre-trained models compare
to baseline networks trained from scratch, and whether a close relation of a downstreamtask and pre-training task exhibit higher learning effects than general audio embeddings
like OpenL3 [3] that were not trained on a related MIR task at all.

2

Related Work

Current approaches for SMC mostly rely on deep neural networks (DNN) trained andoptimized using raw audio data or its time-frequency transform. The most popular networks for this task are convolutional neural networks (CNN) [12, 13, 5, 20]. In 2015
Lidy et. al [13] used a CNN approach consisting of one convolutional layer followed
by a fully connected layer achieving 99.7% accuracy on binary classification of speech
and music at the MIREX competition [18]. The separate detection of both classes stillachieved 88.5% accuracy. The model proposed by Marolt [15] obtained an accuracy of
98% for SMC, and 92% for a 4-class classification for speech, solo singing, choir, andinstrumental music. The model uses a combination of convolutional layers followedby residual layers. Besides the GTZAN [25] and MUSAN [24] datasets, additional fieldrecordings and traditional music from various libraries were included. In [4], different architectures including DNNs, CNNs and recurrent neural networks were evaluatedfor speech music detection. According to their findings, a model with six CNN layers performed best on AudioSet [21] with 86% accuracy for speech or music detection.
SwishNet [8] uses a set of one-dimensional convolutions with multiple skip connections
on Mel-Frequency Cepstral Coefficients (MFCCs). This model achieved 93% accuracy
on a 3-class detection task with speech, music, and noise and 99% accuracy for speechdetection using the MUSAN [24] dataset for training and GTZAN [25] for verification.
For performance comparison Hussain et al. used a Gaussian Mixture Model, a fully
connected neural network (FCN), and a transfer learning approach of the MobileNet architecture [7] was used. The MobileNet embeddings worked best throughout the paper
followed by the proposed SwishNet architecture.
Choi et al. [2] showed that transfer learning can outperform traditional feature based
methods in many different MIR tasks as well as audio event detection (AED). In [3]OpenL3 embeddings were trained on the task of audio-video correspondence in a selfsupervised manner inspired by [1] and subsequently transferred to the task of environmental sound classification. On several AED datasets this approach outperformed other
TL embeddings based on VGG-like and SoundNet architectures. Grollmisch et al. [6]verified the potential of OpenL3 for different MIR and industrial sound analysis tasks.
The embeddings consistently resulted in good classification performance while otherembeddings highly varied depending on the task. Kong et al. [11] proposed pre-trained
audio neural networks (PANN) for transfer learning. The authors introduce an input rep-84

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

resentation called Wavegram, a neural network based time-frequency-transformation.
A multi-layer CNN is connected to this input network and trained for audio tagging
on the AudioSet [21]. Subsequently, these embeddings were augmented by trainableclassifiers and applied to six different classification tasks including genre and acoustic
scenes classification, among others. In most of these tasks, the embeddings performed
better or similar to state-of-the-art approaches. The authors compared multiple networks
and depths as well as different positions for unfreezing of the pre-trained embeddings
concluding that a complete fine-tuning of all network parameters results in the highest
accuracy. To overcome the overfitting to one particular task Kim et al. [9] proposed
multi-task learning. During training, a CNN network structure is split at one stage inthe model into multiple branches, one for each task. All branches consist of the same
network architecture and where trained simultaneously. The last layers before the classifiers of each branch are concatenated and used as combined embeddings. Initially thesystem was trained on the Million Song Database [16] for tempo estimation and song
similarity. The embeddings were evaluated on target tasks like genre classification ormusic recommendation. Different branch positions in the network were evaluated concluding that earlier branching results in better performance for the target tasks but also
in bigger networks with more computational costs.

3

Datasets

To get a better understanding of the performance of the evaluated architectures, four
datasets were used during training as depicted in table 1. The MUSAN dataset [24] andthe GTZAN dataset [25] consist of clearly distinguishable broadcast material of western music and speech. In addition, two more challenging ethnomusicology datasets areincluded. The Marolt19 dataset was first introduced in [15]. Apart from the speechclass, choir, solo singing and instrumental music are combined into the ’music’ class fortraining. Marolt19 includes material from archives such as the British Library world& traditional music collection, the French Centre of Scientific Research (CNRS), or the
Slovenian sound archive Ethnomuse. The ACMus Youtube Dataset (ACMusYT)4
was collected as part of the ACMus research project. 5 It consists of audio excerpts of
4
5

https://zenodo.org/record/4870820
ACMus project page: https://acmus-mir.github.io/

Table 1. Characteristics of the datasets used for training on speech/music classification (sourcetask) and for transfer learning tasks (target tasks).
Application Dataset ID Classes [Number of Files per class]
Sample Rate Bit Depth Duration [min]MUSANMusic [660], Speech [426], Noise [764]16 kHz
16
6483
GTZAN
Music [64], Speech [64]22 kHz16
64
Training
Solo Singing [1512], Choir [1618],
Marolt19
44 kHz16
577
Instrumental [2960], Speech [1284]
ACMusYT Speech [40], Music [35], A Cappella [40]48 kHz16
88
S&S
Music [101], Speech [80]22 kHz16
45
Transfer ACMusVF Male [46], Female [24]96 kHz24
26
ACMusIF 1 [43], 2 [42], 3 [43], 4 [21], 5+ [36]96 kHz24
65

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

traditional Colombian music from the Andes region. The subset used in this work consists of two classes: speech and music with vocals. The ’vocal-only’ class is not usedin these experiments for better separation during training. For TL experiments, the pretrained networks are subsequently fine-tuned with separate datasets. An established set
for speech music tasks is the Slaney & Scheirer dataset (S&S) [23] with content takenfrom broadcast material. All 64 files of noise and mixed (speech/music) content are excluded before the evaluation. From the ACMus-MIR dataset [17], the Instrumental
Format Set (ACMusIF) was used. This set was created from traditional Andean
music recordings for the purpose of ensemble size classification. The goal of this task
is to classify music tracks as solo, duo, trio, quartet, and larger ensembles. Finally, theACMus Vocal Format Set (ACMusVF) is included.6 It comprises Andean vocal music (male and female singers) partly with accompaniment.

4

Methodology

4.1

Network Architectures

The INA (Institut National de l’Audiovisuel) approach [5] is a CNN-based network thatuses 68 frames of 21 MFCCs with a maximum frequency of 4 kHz as input representation to four 2D-convolutional layers followed by four dense layers with dropout. Eachof these layers are followed by batch normalization and a ReLU activation. The output
layer uses Sof tmax activation (see Figure 1 for details). INA achieved an average accuracy of 92.6% at the 2018 MIREX [19] competition on music detection and 96.2%
on speech detection.
SwishNet is an architecture based on one-dimensional convolutional layers in combination with residual and skip connections [8] (see Figure 2). As input, 16 frames of
22 MFCCs are extracted from one second audio snippets and used as 2D feature representation. Classification results range from 93% frame-wise accuracy for 3 classes(speech, music, noise) to 99% segment-wise accuracy for speech detection.
VGG-like architectures are commonly used networks in many fields of deep learning [15, 3, 2]. The network illustrated in Figure 3 is inspired by [22]. Logarithmic MelSpectrogram (MelSpec) is used as input from audio sampled at 22050 Hz. Frames of2048 samples with 512 samples hop size are transformed to 128 mel band representation. A patch of 10 frames is fed to four convolutional layers with 32 kernels of size 3x3.
6

https://zenodo.org/record/4791394

Fig. 1. INA network architecture [5]. The green line indicates the freezing point of the intermediate fine-tuning strategy. The red line indicates the output point of the embedding vector.

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

Fig. 2. SwishNet network architecture. The green line indicates the freezing point of the intermediate fine-tuning strategy. The red line indicates the output point of the embedding vector. Referto [8] for more details on the architecture.

Each layer is followed by batch normalization and ReLU activation. After every second
convolutional layer, MaxPooling is applied with a 3x3 window. Two fully connected
layers are added after flattening followed by the classifier with a Sof tmax activation.
OpenL3 embeddings are included as a state-of-the-art baseline. The 512 unit feature
vectors are extracted from the audio data with default parameters from [3]. These vectors are normalized between 0 and 1 and used as input for a trainable neural classifierconsisting of a 128 unit dense layer followed by the final classifier with Sigmoid activation. As a second baseline, a simple DNN architecture is used. MelSpecs with equalmeasures as for SwishNet and VGG-like models are input and passed through one dense
layer with 128 units and the output layer. The same structure is used for the appended
classifiers of the computed embeddings in Section 4.4 and hence gives an insight into
the learning effects of the preceded architectures. Adam is used as optimization and
Sof tmax as activation function.
4.2

Input Representation

All datasets were normalized in a range of [-1, 1] in time domain and unified to asampling rate of 22050 Hz and 16 bits. The MelSpec representation with 128 bands
and 512 hop size is evaluated as input representation for all networks. Additionally theoriginal MFCC input representations of the SwishNet and INA approach are included
to check for side effects of the input adaption. The original VGG-like approach alreadyFig. 3. VGG-like network architecture. The green line indicates the freezing point of the intermediate fine-tuning strategy. The red line indicates the output point of the embedding vector.

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

used MelSpecs. The OpenL3 embeddings create batches of features with a feature sizeof 512 samples (see Section 4.1) from 100 ms audio frames.
4.3

Implementation Details and Metrics

In all experiments, 10% of the data is used for testing, and 10% for validation. All experiments are repeated using five-fold cross-validation. All data is balanced by random
down-sampling. After transforming the input to MelSpec, it is normalized feature-wise
to zero mean in the range from -1 to 1 and concatenated to batches of 64 frames. Each
network is trained for 200 epochs with the option for early stopping if the validation
accuracy does not increase for 50 epochs. The Adam optimizer [10] with a learning
rate of 10−3 is used for all architectures for best comparability to the original implementations. Results are presented as the mean accuracy over 5 cross-validation folds
with its standard deviation.
4.4

Transfer Learning Networks and Tasks

For transfer learning, the models are trained with a balanced combination of all four
training sets. Afterwards the output layers are removed from the trained networks (seeSection 4.1) and the remaining layers are fixed and used for embedding calculation. A
trainable classifier is appended consisting of a 128 unit dense layer and a dense output
layer matching the number of the target task classes. Three different freezing positions
for the trained models are evaluated. In the first strategy, only the classifier is trained
while the network weights remain fixed. The second strategy unfreezes the networks in
an intermediate position so the classifier and parts of the networks are fine-tuned. These
positions are illustrated green in Figures 1, 2, and 3, respectively. In a third strategy, all
network weights are unfrozen and fine-tuned along with the classifier. These strategies
do not apply for OpenL3 because of its baseline function. As transfer learning tasks, weevaluate the following target tasks: (a) SMC with S&S dataset, (b) accompaniment detection with ACMusVF dataset. The goal of this task is to distinguish music pieces with
instrumental accompaniment from vocal-only performances, (c) female vs male singer
classification on the ACMusVF dataset. We refer to this task as gender classification in
singing, (d) ensemble size classification on the ACMusIF set.

5

Results

5.1

Network Architectures Comparison

Figure 4 shows the mean file-wise and frame-wise results over all training sets foreach architecture. Results show that OpenL3 embeddings work well on all datasets
for SMC. Looking at the frame-wise accuracy, SwishNet is slightly below the remaining two CNN-based architectures by around 3%. Figure 5 presents results for binary
SMC and a three-class task which includes noise as the third class. This is performed
for the MUSAN and Marolt19 datasets where noise samples are included. Marolt19
appears to be the most challenging set due to the fact that it does not only consist of88

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

Fig. 4. Comparison of the mean frame-wise accuracy per architecture for speech/music classification averaged over all training sets (MUSAN, GTZAN, Marlot19, ACMusYT).

broadcast material unlike MUSAN. As expected, the accuracy drops for a more complex
task of three classes. The highest drop of 24.3 % occurs for INA in connection with
MelSpec input followed by the VGG-like model. For MUSAN the most significant drop
can be observed for the INA model in connection with MFCC input. The varying results indicate that the INA architecture might not be well suited for alternative tasks
in contrast to OpenL3 which shows best robustness. Regarding the input representation no significant performance differences can be observed in Figure 4. Only a slight
improvement for MelSpecs is visible. Figure 5 confirms this trend as MelSpecs have aslightly better performance on average. In conclusion MFCCs can increase performancefor specific tasks but MelSpecs have a more robust behavior in general hence MelSpec
is used for further experiments.

Fig. 5. Comparison of frame-based accuracy for binary classification versus 3-class classification.
Results are shown for MUSAN (yellow) and Marolt19 (blue) datasets.

5.2

Transfer Learning

Results for all transfer experiments are presented in Table 2. Besides the three networkarchitectures (INA, SwishNet, and VGG-like), results for the OpenL3 embeddings and89

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

the DNN baselines are shown. In general the resulting models tend to overfit duringfine-tuning due to the small training data.
Speech Music Classification with S&S: In this experiment, the target task for TL
was kept the same so models are only transferred to an unseen dataset. In Table 2 a learning effect from the pre-training can be observed for the Slaney & Scheirer dataset. In
detail embeddings from INA and VGG-like models can make better use of pre-training
and gain up to 3 % classification accuracy while the performance of SwishNet remains
at almost the same level. OpenL3 embeddings outperform all other models for thisdataset-task combination.
Table 2. Transfer learning results. Accuracy values are presented for fully frozen (AccF Z ), partly
trainable (intermediate) (AccIN ), and the fully trainable embeddings (AccF T ). Listed are theresults for each architecure using their pre-trained embeddings (Emb) as well as their original
network trained from scratch on the according task (Orig). In addition OpenL3 embeddings andthe two-layer DNN (see 4.1) are listed as baseline.
Task-Set-Combination

Model

IN AEmb
IN AOrig
V GG − likeEmb
V GG − likeOrig
Speech Music on S&S
SwishN etEmb
SwishN etOrig
OpenL3Emb
DN Nbaseline
IN AEmb
IN AOrig
V GG − likeEmb
V GG − likeOrig
Accompaniment on ACMus VF
SwishN etEmb
SwishN etOrig
OpenL3Emb
DN Nbaseline
IN AEmb
IN AOrig
V GG − likeEmb
V GG − likeOrig
Gender on ACMus VF
SwishN etEmb
SwishN etOrig
OpenL3Emb
DN Nbaseline
IN AEmb
IN AOrig
V GG − likeEmb
V GG − likeOrig
Ensemble Size on ACMus IF
SwishN etEmb
SwishN etOrig
OpenL3Emb
DN Nbaseline

AccF Z [%] AccIN [%] AccF T [%]98,8 ± 1,4
97,4 ± 1,5
92,3 ± 2,799,2 ± 0,4
85,2 ± 5,6
88,5 ± 7,4
81,5 ± 4,6
99,6 ± 0,570,0 ± 7,7
71,8 ± 5,2
72,6 ± 5,072,3 ± 9,6
49,8 ± 5,6
49,7 ± 5,046,7 ± 5,7
76,2 ± 4,4-

97,6 ± 2,1 85,1 ± 4,8
93,8 ± 3,097,9 ± 1,9 88,9 ± 1,6
95,1 ± 2,1
93,0 ± 2,4 95,0 ± 1,7
92,9 ± 1,5
92,9 ± 1,982,5 ± 9,1 90,8 ± 5,2
80,2 ± 6,6
94,9 ± 3,2 92,7 ± 5,8
92,7 ± 4,985,1 ± 4,6 93,6 ± 3,2
94,0 ± 3,7
96,5 ± 1,7
47,3 ± 7,9 59,3 ± 7,6
67,4 ± 7,075,8 ± 9,1 73,5 ± 6,2
73,6 ± 8,1
73,1 ± 5,1 78,3 ± 8,9
74,9 ± 9,5
72,6 ± 10,3
52,1 ± 10,2 56,7 ± 4,5
48,8 ± 7,2
51,3 ± 6,8 47,1 ± 3,9
57,9 ± 5,3
48,7 ± 6,3 54,3 ± 5,4
56,3 ± 5,6
61,4 ± 5,3

Accompaniment detection on ACMusVF: For this task OpenL3 again shows best
results and is followed by the VGG-like embeddings with a performance gap of around
11 %. Despite the close task relation to SMC no architecture overcomes the accuracyof the plain DNN and hence no learning effect from TL is achieved in connection with90

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

this task. This is reinforced by the fact that for SwishNet and VGG-like architectures,the original models perform better than their embedding counterparts.
Female/Male singer classification on ACMusVF: For this task SwishNet embeddings show best results closely followed by OpenL3 embeddings. The original networks
for each model show comparable or better performances compared to the fully frozen
embeddings indicating that no learning effect of pre-training is visible. Again the DNN
performs comparable to the best model refuting a benefit of the knowledge transfer.
Ensemble size classification on ACMus-MIR: All created embeddings perform
similar with nearly 50 % accuracy. The baseline architectures of VGG-like and SwishNet show better results when trained from scratch excluding the idea of a possible learning effect. This is confirmed by the plain DNN baseline that outperformed the embeddings by around 12 %. The usage of embeddings results in a inverse effect for this task.
Furthermore this experiment engages the most unrelated task relative to SMC in the setof transfer tasks. The best results are achieved using the unrelated OpenL3 embeddings
with 76.2 %. A file-wise evaluation of OpenL3 results in 84 % accuracy which confirms
the outcome from Grollmisch et al. [6].
Freezing strategies: Inspecting the last two rows of each embedding in table 2
gives insights to freezing strategies for the pre-trained networks. With more degree of
freedom, meaning more trainable layers, the accuracy tend to increase in most cases.
This trend is highly network-dependent and mainly applies to SwishNet models while
INA tends to be more unstable showing a higher fluctuation. VGG-like models perform
best in intermediate state.

6

Conclusions

This work examines the idea of transfer learning (TL) by creating new feature representations from one source task (pre-training), to use them as embeddings for several target MIR tasks. Three network architectures (INA, SwishNet, VGG-like) were initiallytrained for SMC, and subsequently applied to four new classification tasks. Our experiments show a slight dominance of the MelSpec as input representation over MFCCs
during training. No significant performance difference between the three architectures
is visible for the source task while OpenL3 embeddings consistently showed best SMC
accuracy. In comparison to the networks trained from scratch, pre-training results in aslight improvement when used with an additional DNN classifier for the source task.
In the TL experiments, the direct combination of MelSpec input and the DNN classifier surpasses the embedding performance in some cases. These results suggest thatthe learning effect of pre-training is not consistent over all experiments. Furthermore,creating embeddings with tasks closely related to the target tasks show no evident benefit compared to general audio embeddings such as OpenL3, which performed best in
most of the cases. A possible cause can be the self-supervised creation of these embeddings which inhabits limitless availability of training data. However, the amount oftraining data used for pre-training the different embeddings is not considered in theseexperiments and is left for future work.

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

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