Heart rate (HR) estimation using wearable reflectance-type photoplethysmographic (PPG) signals is challenging due to low signal-to-noise ratio (SNR). Especially during intensive exercise, motion artifacts (MAs) overwhelm PPG signals in an unpredictable way. To overcome the issue, an acceleration signal as a reference signal has been adopted by simultaneously measuring with PPG signal. However, MAs are frequently uncorrelated with accelerometer signals under various activities. In this paper, we present a learning-based framework for HR estimation. The proposed framework is based on the deep neural network (DNN). For the feasibility study, we presented a simple network with two fully connected layers. We first extracted power spectra from the measured PPG signal and the acceleration signal. The two power spectra were then used for the input layer in the network. In addition, to inform the PPG signal quality, we added the acceleration signal intensity for the input layer. The proposed simple DNN network was trained for 10 epochs in IEEE Signal Processing Cup 2015 (ISPC) dataset (n=23). Subsequently, the trained network provided low mean absolute error (MAE) of 2.31 bpm in the ISPC dataset. We further tested the network on the new BAMI dataset (n=5), and found that it provided 4.72 bpm of MAE. On the other hand, the MAE without the learning frame was 15.73 bpm. In this study, we found that the simple DNN technique is effective. More training issues were also discussed.