Short-Term Sinusoidal Modeling of an Oriental Music Signal by Using CQT Transform

In this paper, we propose a method for characterizing a musical signal by extracting a set of harmonic descriptors reflecting the maximum information contained in this signal. We focus our study on a signal of oriental music characterized by its richness in tone that can be extended to 1/4 tone, taking into account the frequency and time characteristics of this type of music. To do so, the original signal is slotted and analyzed on a window of short duration. This signal is viewed as the result of a combined modulation of amplitude and frequency. For this result, we apply short-term the non-stationary sinusoidal modeling technique. In each segment, the signal is represented by a set of sinusoids characterized by their intrinsic parameters: amplitudes, frequencies and phases. The modeling approach adopted is closely related to the slot window;therefore great importance is devoted to the study and the choice of the kind of the window and its width. It must be of variable length in order to get better results in the practical implementation of our method. For this purpose, evaluation tests were carried out by synthesizing the signal from the estimated parameters. Interesting results have been identified concerning the comparison of the synthesized signal with the original signal.

高精度复调乐音识别方法

为解决复调乐音频识别分辨率偏低的问题,提出一种基于时频谱的复调乐音识别方法,提取复调乐音主旋律和伴奏,算法音高分辨率远超传统方法的半音阶识别。首先,用短时傅里叶变换(STFT)获得音乐信号时频谱;其次,提出一种自适应边缘失真处理方法,对时频谱进行二值化并降低音符边缘失真;再次,对二值谱进行预定位,应用改进模拟退火(SA)算法将离散域上各类变换转化至连续域,实现音符的精准定位;最后,通过基于密度的聚类算法(DBSCAN)和基频提取方法得到乐音信息。实验结果表明,对于复调音乐片段,所提算法同时具有时频高分辨率,频域平均误差小于1 Hz,时域平均误差小于50 ms。

一种天波超视距雷达电离层相位污染联合校正方法

天波超视距雷达(OTHR)利用电离层的反射实现远距离探测。由于受到电离层时变因素的影响,在长相干积累时间条件下,回波谱会发生展宽,严重影响OTHR对舰船等低速目标的检测能力。针对OTHR回波受到电离层相位污染后正负Bragg展宽峰发生交叠的情况,该文提出了一种联合短时MUSIC变换(STMT)和改进相位梯度(PGA)法的校正方法(即ST_IPGA法)。该方法首先利用STMT变换得到相位污染的粗估计,对污染回波进行初步补偿;然后采用自适应滑窗滤波器提取纯净且完整的海杂波标校信号;最后利用改进的PGA法估计出剩余相位污染,对污染回波进行精确校正。理论分析和仿真结果表明该方法对于校正严重电离层相位污染导致的正负Bragg展宽峰发生交叠的情况具有明显的效果。

一种改进的跳频信号参数估计方法

针对在跳频信号跳变时刻和跳变频率估计方面实时性和估计精度无法同时兼顾的问题,提出了一种基于短时傅立叶变换(STFT)和多重信号分类(MUSIC)算法的跳频信号参数估计方法。在建立跳频信号数学模型的基础上,利用STFT选取较大时间窗对整个信号在时域进行粗搜索,生成时频谱图,提取时频脊线从而获得跳变时刻,然后选取较小时间窗在已知跳变时间段利用STFT进行跳变时刻的细估计,并利用MUSIC算法进行频率的精确估计。该方法利用STFT的二次估计,减少了MUSIC搜索范围,从而降低了时间开销。仿真表明该算法的跳变时刻频率估计精度高,实时性能满足参数测量需求。